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Building an AI Support Tool for Real-time Ulcerative Colitis Diagnosis (2404.08693v1)
Published 10 Apr 2024 in eess.IV
Abstract: Ulcerative Colitis (UC) is a chronic inflammatory bowel disease decreasing life quality through symptoms such as bloody diarrhoea and abdominal pain. Endoscopy is a cornerstone of diagnosis and monitoring of UC. The Mayo endoscopic subscore (MES) index is the standard for measuring UC severity during endoscopic evaluation. However, the MES is subject to high inter-observer variability leading to misdiagnosis and suboptimal treatment. We propose using a machine-learning based MES classification system to support the endoscopic process and to mitigate the observer-variability. The system runs real-time in the clinic and augments doctors' decision-making during the endoscopy. This project report outlines the process of designing, creating and evaluating our system. We describe our initial evaluation, which is a combination of a standard non-clinical model test and a first clinical test of the system on a real patient.
- Ulcerative colitis (Primer). Nature Reviews: Disease Primers. 2020;6(1). Wang et al. [2023] Wang R, Li Z, Liu S, Zhang D. Global, regional and national burden of inflammatory bowel disease in 204 countries and territories from 1990 to 2019: a systematic analysis based on the Global Burden of Disease Study 2019. BMJ Open. 2023;13(3). 10.1136/bmjopen-2022-065186. Preisler et al. [2015] Preisler L, Svendsen M, Nerup N, Svendsen L, Konge L. Simulation-Based Training for Colonoscopy Establishing Criteria for Competency. Medicine. 2015;94(4):e440. Schroeder et al. [1987] Schroeder KW, Tremaine WJ, Ilstrup DM. Coated Oral 5-Aminosalicylic Acid Therapy for Mildly to Moderately Active Ulcerative Colitis. New England Journal of Medicine. 1987;317(26):1625–1629. PMID: 3317057. Samaan et al. [2014] Samaan MA, Mosli MH, Sandborn WJ, Feagan BG, D’Haens GR, Dubcenco E, et al. A Systematic Review of the Measurement of Endoscopic Healing in Ulcerative Colitis Clinical Trials: Recommendations and Implications for Future Research. Inflammatory Bowel Diseases. 2014 05;20(8):1465–1471. Vashist et al. [2018] Vashist N, Samaan M, Mosli M, Parker C, MacDonald J, Nelson S, et al. Endoscopic scoring indices for evaluation of disease activity in ulcerative colitis. Cohrane Database of Systematic Reviews. 2018;1(1):CD011450. Ali [2022] Ali S. Where do we stand in AI for endoscopic image analysis? Deciphering gaps and future directions. npj Digital Medicine. 2022;5(1):184. Lo and Burisch [2021] Lo B, Burisch J. Artificial intelligence assisted assessment of endoscopic disease activity in inflammatory bowel disease. Artificial Intelligence in Gastrointestinal Endoscopy. 2021;2(4):95–102. Lo et al. [2022] Lo B, Liu Z, Bendtsen F, Igel C, Vind I, Burisch J. High accuracy in classifying endoscopic severity in ulcerative colitis using convolutional neural network. The American Journal of Gastroenterology. 2022;117(11):1648–1654. Becker et al. [2021] Becker BG, Arcadu F, Thalhammer A, Serna CG, Feehan O, Drawnel F, et al. Training and deploying a deep learning model for endoscopic severity grading in ulcerative colitis using multicenter clinical trial data. Therapeutic Advances in Gastrointestinal Endoscopy. 2021;14:2631774521990623. Lo et al. [2021] Lo B, Liu Z, Bendtsen F, Igel C, Vind I, Burisch TPP Johan. OP07 Artificial intelligence surpasses gastrointestinal experts in the classification of endoscopic severity among Ulcerative Colitis. Journal of Crohn’s and Colitis. 2021 05;15(Supplement):S007–S007. Vaze et al. [2022] Vaze S, Han K, Vedaldi A, Zisserman A. Open-set recognition: A good closed-set classifier is all you need? In: International Conference on Learning Representations (ICLR); 2022. . Gottlieb et al. [2021] Gottlieb K, Requa J, Karnes W, Chandra Gudivada R, Shen J, Rael E, et al. Central Reading of Ulcerative Colitis Clinical Trial Videos Using Neural Networks. Gastroenterology. 2021;160(3):710–719.e2. Woo et al. [2023] Woo S, Debnath S, Hu R, Chen X, Liu Z, Kweon IS, et al. ConvNeXt V2: Co-designing and Scaling ConvNets with Masked Autoencoders. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR); 2023. p. 6133–16142. Deng et al. [2009] Deng J, Dong W, Socher R, Li LJ, Li K, Fei-Fei L. Imagenet: A large-scale hierarchical image database. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR); 2009. p. 248–255. Guo et al. [2017] Guo C, Pleiss G, Sun Y, Weinberger KQ. On Calibration of Modern Neural Networks. In: Precup O, Teh YW, editors. Proceedings of the 34th International Conference on Machine Learning. vol. 70 of Proceedings of Machine Learning Research. PMLR; 2017. p. 1321–1330. Kishi et al. [2022] Kishi M, Hirai F, Takatsu N, et al. A review on the current status and definitions of activity indices in inflammatory bowel disease: how to use indices for precise evaluation. Journal of Gastroenterology. 2022;14:246–266. Wang R, Li Z, Liu S, Zhang D. Global, regional and national burden of inflammatory bowel disease in 204 countries and territories from 1990 to 2019: a systematic analysis based on the Global Burden of Disease Study 2019. BMJ Open. 2023;13(3). 10.1136/bmjopen-2022-065186. Preisler et al. [2015] Preisler L, Svendsen M, Nerup N, Svendsen L, Konge L. Simulation-Based Training for Colonoscopy Establishing Criteria for Competency. Medicine. 2015;94(4):e440. Schroeder et al. [1987] Schroeder KW, Tremaine WJ, Ilstrup DM. Coated Oral 5-Aminosalicylic Acid Therapy for Mildly to Moderately Active Ulcerative Colitis. New England Journal of Medicine. 1987;317(26):1625–1629. PMID: 3317057. Samaan et al. [2014] Samaan MA, Mosli MH, Sandborn WJ, Feagan BG, D’Haens GR, Dubcenco E, et al. A Systematic Review of the Measurement of Endoscopic Healing in Ulcerative Colitis Clinical Trials: Recommendations and Implications for Future Research. Inflammatory Bowel Diseases. 2014 05;20(8):1465–1471. Vashist et al. [2018] Vashist N, Samaan M, Mosli M, Parker C, MacDonald J, Nelson S, et al. Endoscopic scoring indices for evaluation of disease activity in ulcerative colitis. Cohrane Database of Systematic Reviews. 2018;1(1):CD011450. Ali [2022] Ali S. Where do we stand in AI for endoscopic image analysis? Deciphering gaps and future directions. npj Digital Medicine. 2022;5(1):184. Lo and Burisch [2021] Lo B, Burisch J. Artificial intelligence assisted assessment of endoscopic disease activity in inflammatory bowel disease. Artificial Intelligence in Gastrointestinal Endoscopy. 2021;2(4):95–102. Lo et al. [2022] Lo B, Liu Z, Bendtsen F, Igel C, Vind I, Burisch J. High accuracy in classifying endoscopic severity in ulcerative colitis using convolutional neural network. The American Journal of Gastroenterology. 2022;117(11):1648–1654. Becker et al. [2021] Becker BG, Arcadu F, Thalhammer A, Serna CG, Feehan O, Drawnel F, et al. Training and deploying a deep learning model for endoscopic severity grading in ulcerative colitis using multicenter clinical trial data. Therapeutic Advances in Gastrointestinal Endoscopy. 2021;14:2631774521990623. Lo et al. [2021] Lo B, Liu Z, Bendtsen F, Igel C, Vind I, Burisch TPP Johan. OP07 Artificial intelligence surpasses gastrointestinal experts in the classification of endoscopic severity among Ulcerative Colitis. Journal of Crohn’s and Colitis. 2021 05;15(Supplement):S007–S007. Vaze et al. [2022] Vaze S, Han K, Vedaldi A, Zisserman A. Open-set recognition: A good closed-set classifier is all you need? In: International Conference on Learning Representations (ICLR); 2022. . Gottlieb et al. [2021] Gottlieb K, Requa J, Karnes W, Chandra Gudivada R, Shen J, Rael E, et al. Central Reading of Ulcerative Colitis Clinical Trial Videos Using Neural Networks. Gastroenterology. 2021;160(3):710–719.e2. Woo et al. [2023] Woo S, Debnath S, Hu R, Chen X, Liu Z, Kweon IS, et al. ConvNeXt V2: Co-designing and Scaling ConvNets with Masked Autoencoders. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR); 2023. p. 6133–16142. Deng et al. [2009] Deng J, Dong W, Socher R, Li LJ, Li K, Fei-Fei L. Imagenet: A large-scale hierarchical image database. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR); 2009. p. 248–255. Guo et al. [2017] Guo C, Pleiss G, Sun Y, Weinberger KQ. On Calibration of Modern Neural Networks. In: Precup O, Teh YW, editors. Proceedings of the 34th International Conference on Machine Learning. vol. 70 of Proceedings of Machine Learning Research. PMLR; 2017. p. 1321–1330. Kishi et al. [2022] Kishi M, Hirai F, Takatsu N, et al. A review on the current status and definitions of activity indices in inflammatory bowel disease: how to use indices for precise evaluation. Journal of Gastroenterology. 2022;14:246–266. Preisler L, Svendsen M, Nerup N, Svendsen L, Konge L. Simulation-Based Training for Colonoscopy Establishing Criteria for Competency. Medicine. 2015;94(4):e440. Schroeder et al. [1987] Schroeder KW, Tremaine WJ, Ilstrup DM. Coated Oral 5-Aminosalicylic Acid Therapy for Mildly to Moderately Active Ulcerative Colitis. New England Journal of Medicine. 1987;317(26):1625–1629. PMID: 3317057. Samaan et al. [2014] Samaan MA, Mosli MH, Sandborn WJ, Feagan BG, D’Haens GR, Dubcenco E, et al. A Systematic Review of the Measurement of Endoscopic Healing in Ulcerative Colitis Clinical Trials: Recommendations and Implications for Future Research. Inflammatory Bowel Diseases. 2014 05;20(8):1465–1471. Vashist et al. [2018] Vashist N, Samaan M, Mosli M, Parker C, MacDonald J, Nelson S, et al. Endoscopic scoring indices for evaluation of disease activity in ulcerative colitis. Cohrane Database of Systematic Reviews. 2018;1(1):CD011450. Ali [2022] Ali S. Where do we stand in AI for endoscopic image analysis? Deciphering gaps and future directions. npj Digital Medicine. 2022;5(1):184. Lo and Burisch [2021] Lo B, Burisch J. Artificial intelligence assisted assessment of endoscopic disease activity in inflammatory bowel disease. Artificial Intelligence in Gastrointestinal Endoscopy. 2021;2(4):95–102. Lo et al. [2022] Lo B, Liu Z, Bendtsen F, Igel C, Vind I, Burisch J. High accuracy in classifying endoscopic severity in ulcerative colitis using convolutional neural network. The American Journal of Gastroenterology. 2022;117(11):1648–1654. Becker et al. [2021] Becker BG, Arcadu F, Thalhammer A, Serna CG, Feehan O, Drawnel F, et al. Training and deploying a deep learning model for endoscopic severity grading in ulcerative colitis using multicenter clinical trial data. Therapeutic Advances in Gastrointestinal Endoscopy. 2021;14:2631774521990623. Lo et al. [2021] Lo B, Liu Z, Bendtsen F, Igel C, Vind I, Burisch TPP Johan. OP07 Artificial intelligence surpasses gastrointestinal experts in the classification of endoscopic severity among Ulcerative Colitis. Journal of Crohn’s and Colitis. 2021 05;15(Supplement):S007–S007. Vaze et al. [2022] Vaze S, Han K, Vedaldi A, Zisserman A. Open-set recognition: A good closed-set classifier is all you need? In: International Conference on Learning Representations (ICLR); 2022. . Gottlieb et al. [2021] Gottlieb K, Requa J, Karnes W, Chandra Gudivada R, Shen J, Rael E, et al. Central Reading of Ulcerative Colitis Clinical Trial Videos Using Neural Networks. Gastroenterology. 2021;160(3):710–719.e2. Woo et al. [2023] Woo S, Debnath S, Hu R, Chen X, Liu Z, Kweon IS, et al. ConvNeXt V2: Co-designing and Scaling ConvNets with Masked Autoencoders. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR); 2023. p. 6133–16142. Deng et al. [2009] Deng J, Dong W, Socher R, Li LJ, Li K, Fei-Fei L. Imagenet: A large-scale hierarchical image database. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR); 2009. p. 248–255. Guo et al. [2017] Guo C, Pleiss G, Sun Y, Weinberger KQ. On Calibration of Modern Neural Networks. In: Precup O, Teh YW, editors. Proceedings of the 34th International Conference on Machine Learning. vol. 70 of Proceedings of Machine Learning Research. PMLR; 2017. p. 1321–1330. Kishi et al. [2022] Kishi M, Hirai F, Takatsu N, et al. A review on the current status and definitions of activity indices in inflammatory bowel disease: how to use indices for precise evaluation. Journal of Gastroenterology. 2022;14:246–266. Schroeder KW, Tremaine WJ, Ilstrup DM. Coated Oral 5-Aminosalicylic Acid Therapy for Mildly to Moderately Active Ulcerative Colitis. New England Journal of Medicine. 1987;317(26):1625–1629. PMID: 3317057. Samaan et al. [2014] Samaan MA, Mosli MH, Sandborn WJ, Feagan BG, D’Haens GR, Dubcenco E, et al. A Systematic Review of the Measurement of Endoscopic Healing in Ulcerative Colitis Clinical Trials: Recommendations and Implications for Future Research. Inflammatory Bowel Diseases. 2014 05;20(8):1465–1471. Vashist et al. [2018] Vashist N, Samaan M, Mosli M, Parker C, MacDonald J, Nelson S, et al. Endoscopic scoring indices for evaluation of disease activity in ulcerative colitis. Cohrane Database of Systematic Reviews. 2018;1(1):CD011450. Ali [2022] Ali S. Where do we stand in AI for endoscopic image analysis? Deciphering gaps and future directions. npj Digital Medicine. 2022;5(1):184. Lo and Burisch [2021] Lo B, Burisch J. Artificial intelligence assisted assessment of endoscopic disease activity in inflammatory bowel disease. Artificial Intelligence in Gastrointestinal Endoscopy. 2021;2(4):95–102. Lo et al. [2022] Lo B, Liu Z, Bendtsen F, Igel C, Vind I, Burisch J. High accuracy in classifying endoscopic severity in ulcerative colitis using convolutional neural network. The American Journal of Gastroenterology. 2022;117(11):1648–1654. Becker et al. [2021] Becker BG, Arcadu F, Thalhammer A, Serna CG, Feehan O, Drawnel F, et al. Training and deploying a deep learning model for endoscopic severity grading in ulcerative colitis using multicenter clinical trial data. Therapeutic Advances in Gastrointestinal Endoscopy. 2021;14:2631774521990623. Lo et al. [2021] Lo B, Liu Z, Bendtsen F, Igel C, Vind I, Burisch TPP Johan. OP07 Artificial intelligence surpasses gastrointestinal experts in the classification of endoscopic severity among Ulcerative Colitis. Journal of Crohn’s and Colitis. 2021 05;15(Supplement):S007–S007. Vaze et al. [2022] Vaze S, Han K, Vedaldi A, Zisserman A. Open-set recognition: A good closed-set classifier is all you need? In: International Conference on Learning Representations (ICLR); 2022. . Gottlieb et al. [2021] Gottlieb K, Requa J, Karnes W, Chandra Gudivada R, Shen J, Rael E, et al. Central Reading of Ulcerative Colitis Clinical Trial Videos Using Neural Networks. Gastroenterology. 2021;160(3):710–719.e2. Woo et al. [2023] Woo S, Debnath S, Hu R, Chen X, Liu Z, Kweon IS, et al. ConvNeXt V2: Co-designing and Scaling ConvNets with Masked Autoencoders. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR); 2023. p. 6133–16142. Deng et al. [2009] Deng J, Dong W, Socher R, Li LJ, Li K, Fei-Fei L. Imagenet: A large-scale hierarchical image database. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR); 2009. p. 248–255. Guo et al. [2017] Guo C, Pleiss G, Sun Y, Weinberger KQ. On Calibration of Modern Neural Networks. In: Precup O, Teh YW, editors. Proceedings of the 34th International Conference on Machine Learning. vol. 70 of Proceedings of Machine Learning Research. PMLR; 2017. p. 1321–1330. Kishi et al. [2022] Kishi M, Hirai F, Takatsu N, et al. A review on the current status and definitions of activity indices in inflammatory bowel disease: how to use indices for precise evaluation. Journal of Gastroenterology. 2022;14:246–266. Samaan MA, Mosli MH, Sandborn WJ, Feagan BG, D’Haens GR, Dubcenco E, et al. A Systematic Review of the Measurement of Endoscopic Healing in Ulcerative Colitis Clinical Trials: Recommendations and Implications for Future Research. Inflammatory Bowel Diseases. 2014 05;20(8):1465–1471. Vashist et al. [2018] Vashist N, Samaan M, Mosli M, Parker C, MacDonald J, Nelson S, et al. Endoscopic scoring indices for evaluation of disease activity in ulcerative colitis. Cohrane Database of Systematic Reviews. 2018;1(1):CD011450. Ali [2022] Ali S. Where do we stand in AI for endoscopic image analysis? Deciphering gaps and future directions. npj Digital Medicine. 2022;5(1):184. Lo and Burisch [2021] Lo B, Burisch J. Artificial intelligence assisted assessment of endoscopic disease activity in inflammatory bowel disease. Artificial Intelligence in Gastrointestinal Endoscopy. 2021;2(4):95–102. Lo et al. [2022] Lo B, Liu Z, Bendtsen F, Igel C, Vind I, Burisch J. High accuracy in classifying endoscopic severity in ulcerative colitis using convolutional neural network. The American Journal of Gastroenterology. 2022;117(11):1648–1654. Becker et al. [2021] Becker BG, Arcadu F, Thalhammer A, Serna CG, Feehan O, Drawnel F, et al. Training and deploying a deep learning model for endoscopic severity grading in ulcerative colitis using multicenter clinical trial data. Therapeutic Advances in Gastrointestinal Endoscopy. 2021;14:2631774521990623. Lo et al. [2021] Lo B, Liu Z, Bendtsen F, Igel C, Vind I, Burisch TPP Johan. OP07 Artificial intelligence surpasses gastrointestinal experts in the classification of endoscopic severity among Ulcerative Colitis. Journal of Crohn’s and Colitis. 2021 05;15(Supplement):S007–S007. Vaze et al. [2022] Vaze S, Han K, Vedaldi A, Zisserman A. Open-set recognition: A good closed-set classifier is all you need? In: International Conference on Learning Representations (ICLR); 2022. . Gottlieb et al. [2021] Gottlieb K, Requa J, Karnes W, Chandra Gudivada R, Shen J, Rael E, et al. Central Reading of Ulcerative Colitis Clinical Trial Videos Using Neural Networks. Gastroenterology. 2021;160(3):710–719.e2. Woo et al. [2023] Woo S, Debnath S, Hu R, Chen X, Liu Z, Kweon IS, et al. ConvNeXt V2: Co-designing and Scaling ConvNets with Masked Autoencoders. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR); 2023. p. 6133–16142. Deng et al. [2009] Deng J, Dong W, Socher R, Li LJ, Li K, Fei-Fei L. Imagenet: A large-scale hierarchical image database. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR); 2009. p. 248–255. Guo et al. [2017] Guo C, Pleiss G, Sun Y, Weinberger KQ. On Calibration of Modern Neural Networks. In: Precup O, Teh YW, editors. Proceedings of the 34th International Conference on Machine Learning. vol. 70 of Proceedings of Machine Learning Research. PMLR; 2017. p. 1321–1330. Kishi et al. [2022] Kishi M, Hirai F, Takatsu N, et al. A review on the current status and definitions of activity indices in inflammatory bowel disease: how to use indices for precise evaluation. Journal of Gastroenterology. 2022;14:246–266. Vashist N, Samaan M, Mosli M, Parker C, MacDonald J, Nelson S, et al. Endoscopic scoring indices for evaluation of disease activity in ulcerative colitis. Cohrane Database of Systematic Reviews. 2018;1(1):CD011450. Ali [2022] Ali S. Where do we stand in AI for endoscopic image analysis? Deciphering gaps and future directions. npj Digital Medicine. 2022;5(1):184. Lo and Burisch [2021] Lo B, Burisch J. Artificial intelligence assisted assessment of endoscopic disease activity in inflammatory bowel disease. Artificial Intelligence in Gastrointestinal Endoscopy. 2021;2(4):95–102. Lo et al. [2022] Lo B, Liu Z, Bendtsen F, Igel C, Vind I, Burisch J. High accuracy in classifying endoscopic severity in ulcerative colitis using convolutional neural network. The American Journal of Gastroenterology. 2022;117(11):1648–1654. Becker et al. [2021] Becker BG, Arcadu F, Thalhammer A, Serna CG, Feehan O, Drawnel F, et al. Training and deploying a deep learning model for endoscopic severity grading in ulcerative colitis using multicenter clinical trial data. Therapeutic Advances in Gastrointestinal Endoscopy. 2021;14:2631774521990623. Lo et al. [2021] Lo B, Liu Z, Bendtsen F, Igel C, Vind I, Burisch TPP Johan. OP07 Artificial intelligence surpasses gastrointestinal experts in the classification of endoscopic severity among Ulcerative Colitis. Journal of Crohn’s and Colitis. 2021 05;15(Supplement):S007–S007. Vaze et al. [2022] Vaze S, Han K, Vedaldi A, Zisserman A. Open-set recognition: A good closed-set classifier is all you need? In: International Conference on Learning Representations (ICLR); 2022. . Gottlieb et al. [2021] Gottlieb K, Requa J, Karnes W, Chandra Gudivada R, Shen J, Rael E, et al. Central Reading of Ulcerative Colitis Clinical Trial Videos Using Neural Networks. Gastroenterology. 2021;160(3):710–719.e2. Woo et al. [2023] Woo S, Debnath S, Hu R, Chen X, Liu Z, Kweon IS, et al. ConvNeXt V2: Co-designing and Scaling ConvNets with Masked Autoencoders. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR); 2023. p. 6133–16142. Deng et al. [2009] Deng J, Dong W, Socher R, Li LJ, Li K, Fei-Fei L. Imagenet: A large-scale hierarchical image database. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR); 2009. p. 248–255. Guo et al. [2017] Guo C, Pleiss G, Sun Y, Weinberger KQ. On Calibration of Modern Neural Networks. In: Precup O, Teh YW, editors. Proceedings of the 34th International Conference on Machine Learning. vol. 70 of Proceedings of Machine Learning Research. PMLR; 2017. p. 1321–1330. Kishi et al. [2022] Kishi M, Hirai F, Takatsu N, et al. A review on the current status and definitions of activity indices in inflammatory bowel disease: how to use indices for precise evaluation. Journal of Gastroenterology. 2022;14:246–266. Ali S. Where do we stand in AI for endoscopic image analysis? Deciphering gaps and future directions. npj Digital Medicine. 2022;5(1):184. Lo and Burisch [2021] Lo B, Burisch J. Artificial intelligence assisted assessment of endoscopic disease activity in inflammatory bowel disease. Artificial Intelligence in Gastrointestinal Endoscopy. 2021;2(4):95–102. Lo et al. [2022] Lo B, Liu Z, Bendtsen F, Igel C, Vind I, Burisch J. High accuracy in classifying endoscopic severity in ulcerative colitis using convolutional neural network. The American Journal of Gastroenterology. 2022;117(11):1648–1654. Becker et al. [2021] Becker BG, Arcadu F, Thalhammer A, Serna CG, Feehan O, Drawnel F, et al. Training and deploying a deep learning model for endoscopic severity grading in ulcerative colitis using multicenter clinical trial data. Therapeutic Advances in Gastrointestinal Endoscopy. 2021;14:2631774521990623. Lo et al. [2021] Lo B, Liu Z, Bendtsen F, Igel C, Vind I, Burisch TPP Johan. OP07 Artificial intelligence surpasses gastrointestinal experts in the classification of endoscopic severity among Ulcerative Colitis. Journal of Crohn’s and Colitis. 2021 05;15(Supplement):S007–S007. Vaze et al. [2022] Vaze S, Han K, Vedaldi A, Zisserman A. Open-set recognition: A good closed-set classifier is all you need? In: International Conference on Learning Representations (ICLR); 2022. . Gottlieb et al. [2021] Gottlieb K, Requa J, Karnes W, Chandra Gudivada R, Shen J, Rael E, et al. Central Reading of Ulcerative Colitis Clinical Trial Videos Using Neural Networks. Gastroenterology. 2021;160(3):710–719.e2. Woo et al. [2023] Woo S, Debnath S, Hu R, Chen X, Liu Z, Kweon IS, et al. ConvNeXt V2: Co-designing and Scaling ConvNets with Masked Autoencoders. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR); 2023. p. 6133–16142. Deng et al. [2009] Deng J, Dong W, Socher R, Li LJ, Li K, Fei-Fei L. Imagenet: A large-scale hierarchical image database. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR); 2009. p. 248–255. Guo et al. [2017] Guo C, Pleiss G, Sun Y, Weinberger KQ. On Calibration of Modern Neural Networks. In: Precup O, Teh YW, editors. Proceedings of the 34th International Conference on Machine Learning. vol. 70 of Proceedings of Machine Learning Research. PMLR; 2017. p. 1321–1330. Kishi et al. [2022] Kishi M, Hirai F, Takatsu N, et al. A review on the current status and definitions of activity indices in inflammatory bowel disease: how to use indices for precise evaluation. Journal of Gastroenterology. 2022;14:246–266. Lo B, Burisch J. Artificial intelligence assisted assessment of endoscopic disease activity in inflammatory bowel disease. Artificial Intelligence in Gastrointestinal Endoscopy. 2021;2(4):95–102. Lo et al. [2022] Lo B, Liu Z, Bendtsen F, Igel C, Vind I, Burisch J. High accuracy in classifying endoscopic severity in ulcerative colitis using convolutional neural network. The American Journal of Gastroenterology. 2022;117(11):1648–1654. Becker et al. [2021] Becker BG, Arcadu F, Thalhammer A, Serna CG, Feehan O, Drawnel F, et al. Training and deploying a deep learning model for endoscopic severity grading in ulcerative colitis using multicenter clinical trial data. Therapeutic Advances in Gastrointestinal Endoscopy. 2021;14:2631774521990623. Lo et al. [2021] Lo B, Liu Z, Bendtsen F, Igel C, Vind I, Burisch TPP Johan. OP07 Artificial intelligence surpasses gastrointestinal experts in the classification of endoscopic severity among Ulcerative Colitis. Journal of Crohn’s and Colitis. 2021 05;15(Supplement):S007–S007. Vaze et al. [2022] Vaze S, Han K, Vedaldi A, Zisserman A. Open-set recognition: A good closed-set classifier is all you need? In: International Conference on Learning Representations (ICLR); 2022. . Gottlieb et al. [2021] Gottlieb K, Requa J, Karnes W, Chandra Gudivada R, Shen J, Rael E, et al. Central Reading of Ulcerative Colitis Clinical Trial Videos Using Neural Networks. Gastroenterology. 2021;160(3):710–719.e2. Woo et al. [2023] Woo S, Debnath S, Hu R, Chen X, Liu Z, Kweon IS, et al. ConvNeXt V2: Co-designing and Scaling ConvNets with Masked Autoencoders. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR); 2023. p. 6133–16142. Deng et al. [2009] Deng J, Dong W, Socher R, Li LJ, Li K, Fei-Fei L. Imagenet: A large-scale hierarchical image database. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR); 2009. p. 248–255. Guo et al. [2017] Guo C, Pleiss G, Sun Y, Weinberger KQ. On Calibration of Modern Neural Networks. In: Precup O, Teh YW, editors. Proceedings of the 34th International Conference on Machine Learning. vol. 70 of Proceedings of Machine Learning Research. PMLR; 2017. p. 1321–1330. Kishi et al. [2022] Kishi M, Hirai F, Takatsu N, et al. A review on the current status and definitions of activity indices in inflammatory bowel disease: how to use indices for precise evaluation. Journal of Gastroenterology. 2022;14:246–266. Lo B, Liu Z, Bendtsen F, Igel C, Vind I, Burisch J. High accuracy in classifying endoscopic severity in ulcerative colitis using convolutional neural network. The American Journal of Gastroenterology. 2022;117(11):1648–1654. Becker et al. [2021] Becker BG, Arcadu F, Thalhammer A, Serna CG, Feehan O, Drawnel F, et al. Training and deploying a deep learning model for endoscopic severity grading in ulcerative colitis using multicenter clinical trial data. Therapeutic Advances in Gastrointestinal Endoscopy. 2021;14:2631774521990623. Lo et al. [2021] Lo B, Liu Z, Bendtsen F, Igel C, Vind I, Burisch TPP Johan. OP07 Artificial intelligence surpasses gastrointestinal experts in the classification of endoscopic severity among Ulcerative Colitis. Journal of Crohn’s and Colitis. 2021 05;15(Supplement):S007–S007. Vaze et al. [2022] Vaze S, Han K, Vedaldi A, Zisserman A. Open-set recognition: A good closed-set classifier is all you need? In: International Conference on Learning Representations (ICLR); 2022. . Gottlieb et al. [2021] Gottlieb K, Requa J, Karnes W, Chandra Gudivada R, Shen J, Rael E, et al. Central Reading of Ulcerative Colitis Clinical Trial Videos Using Neural Networks. Gastroenterology. 2021;160(3):710–719.e2. Woo et al. [2023] Woo S, Debnath S, Hu R, Chen X, Liu Z, Kweon IS, et al. ConvNeXt V2: Co-designing and Scaling ConvNets with Masked Autoencoders. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR); 2023. p. 6133–16142. Deng et al. [2009] Deng J, Dong W, Socher R, Li LJ, Li K, Fei-Fei L. Imagenet: A large-scale hierarchical image database. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR); 2009. p. 248–255. Guo et al. [2017] Guo C, Pleiss G, Sun Y, Weinberger KQ. On Calibration of Modern Neural Networks. In: Precup O, Teh YW, editors. Proceedings of the 34th International Conference on Machine Learning. vol. 70 of Proceedings of Machine Learning Research. PMLR; 2017. p. 1321–1330. Kishi et al. [2022] Kishi M, Hirai F, Takatsu N, et al. A review on the current status and definitions of activity indices in inflammatory bowel disease: how to use indices for precise evaluation. Journal of Gastroenterology. 2022;14:246–266. Becker BG, Arcadu F, Thalhammer A, Serna CG, Feehan O, Drawnel F, et al. Training and deploying a deep learning model for endoscopic severity grading in ulcerative colitis using multicenter clinical trial data. Therapeutic Advances in Gastrointestinal Endoscopy. 2021;14:2631774521990623. Lo et al. [2021] Lo B, Liu Z, Bendtsen F, Igel C, Vind I, Burisch TPP Johan. OP07 Artificial intelligence surpasses gastrointestinal experts in the classification of endoscopic severity among Ulcerative Colitis. Journal of Crohn’s and Colitis. 2021 05;15(Supplement):S007–S007. Vaze et al. [2022] Vaze S, Han K, Vedaldi A, Zisserman A. Open-set recognition: A good closed-set classifier is all you need? In: International Conference on Learning Representations (ICLR); 2022. . Gottlieb et al. [2021] Gottlieb K, Requa J, Karnes W, Chandra Gudivada R, Shen J, Rael E, et al. Central Reading of Ulcerative Colitis Clinical Trial Videos Using Neural Networks. Gastroenterology. 2021;160(3):710–719.e2. Woo et al. [2023] Woo S, Debnath S, Hu R, Chen X, Liu Z, Kweon IS, et al. ConvNeXt V2: Co-designing and Scaling ConvNets with Masked Autoencoders. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR); 2023. p. 6133–16142. Deng et al. [2009] Deng J, Dong W, Socher R, Li LJ, Li K, Fei-Fei L. Imagenet: A large-scale hierarchical image database. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR); 2009. p. 248–255. Guo et al. [2017] Guo C, Pleiss G, Sun Y, Weinberger KQ. On Calibration of Modern Neural Networks. In: Precup O, Teh YW, editors. Proceedings of the 34th International Conference on Machine Learning. vol. 70 of Proceedings of Machine Learning Research. PMLR; 2017. p. 1321–1330. Kishi et al. [2022] Kishi M, Hirai F, Takatsu N, et al. A review on the current status and definitions of activity indices in inflammatory bowel disease: how to use indices for precise evaluation. Journal of Gastroenterology. 2022;14:246–266. Lo B, Liu Z, Bendtsen F, Igel C, Vind I, Burisch TPP Johan. OP07 Artificial intelligence surpasses gastrointestinal experts in the classification of endoscopic severity among Ulcerative Colitis. Journal of Crohn’s and Colitis. 2021 05;15(Supplement):S007–S007. Vaze et al. [2022] Vaze S, Han K, Vedaldi A, Zisserman A. Open-set recognition: A good closed-set classifier is all you need? In: International Conference on Learning Representations (ICLR); 2022. . Gottlieb et al. [2021] Gottlieb K, Requa J, Karnes W, Chandra Gudivada R, Shen J, Rael E, et al. Central Reading of Ulcerative Colitis Clinical Trial Videos Using Neural Networks. Gastroenterology. 2021;160(3):710–719.e2. Woo et al. [2023] Woo S, Debnath S, Hu R, Chen X, Liu Z, Kweon IS, et al. ConvNeXt V2: Co-designing and Scaling ConvNets with Masked Autoencoders. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR); 2023. p. 6133–16142. Deng et al. [2009] Deng J, Dong W, Socher R, Li LJ, Li K, Fei-Fei L. Imagenet: A large-scale hierarchical image database. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR); 2009. p. 248–255. Guo et al. [2017] Guo C, Pleiss G, Sun Y, Weinberger KQ. On Calibration of Modern Neural Networks. In: Precup O, Teh YW, editors. Proceedings of the 34th International Conference on Machine Learning. vol. 70 of Proceedings of Machine Learning Research. PMLR; 2017. p. 1321–1330. Kishi et al. [2022] Kishi M, Hirai F, Takatsu N, et al. A review on the current status and definitions of activity indices in inflammatory bowel disease: how to use indices for precise evaluation. Journal of Gastroenterology. 2022;14:246–266. Vaze S, Han K, Vedaldi A, Zisserman A. Open-set recognition: A good closed-set classifier is all you need? In: International Conference on Learning Representations (ICLR); 2022. . Gottlieb et al. [2021] Gottlieb K, Requa J, Karnes W, Chandra Gudivada R, Shen J, Rael E, et al. Central Reading of Ulcerative Colitis Clinical Trial Videos Using Neural Networks. Gastroenterology. 2021;160(3):710–719.e2. Woo et al. [2023] Woo S, Debnath S, Hu R, Chen X, Liu Z, Kweon IS, et al. ConvNeXt V2: Co-designing and Scaling ConvNets with Masked Autoencoders. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR); 2023. p. 6133–16142. Deng et al. [2009] Deng J, Dong W, Socher R, Li LJ, Li K, Fei-Fei L. Imagenet: A large-scale hierarchical image database. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR); 2009. p. 248–255. Guo et al. [2017] Guo C, Pleiss G, Sun Y, Weinberger KQ. On Calibration of Modern Neural Networks. In: Precup O, Teh YW, editors. Proceedings of the 34th International Conference on Machine Learning. vol. 70 of Proceedings of Machine Learning Research. PMLR; 2017. p. 1321–1330. Kishi et al. [2022] Kishi M, Hirai F, Takatsu N, et al. A review on the current status and definitions of activity indices in inflammatory bowel disease: how to use indices for precise evaluation. Journal of Gastroenterology. 2022;14:246–266. Gottlieb K, Requa J, Karnes W, Chandra Gudivada R, Shen J, Rael E, et al. Central Reading of Ulcerative Colitis Clinical Trial Videos Using Neural Networks. Gastroenterology. 2021;160(3):710–719.e2. Woo et al. [2023] Woo S, Debnath S, Hu R, Chen X, Liu Z, Kweon IS, et al. ConvNeXt V2: Co-designing and Scaling ConvNets with Masked Autoencoders. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR); 2023. p. 6133–16142. Deng et al. [2009] Deng J, Dong W, Socher R, Li LJ, Li K, Fei-Fei L. Imagenet: A large-scale hierarchical image database. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR); 2009. p. 248–255. Guo et al. [2017] Guo C, Pleiss G, Sun Y, Weinberger KQ. On Calibration of Modern Neural Networks. In: Precup O, Teh YW, editors. Proceedings of the 34th International Conference on Machine Learning. vol. 70 of Proceedings of Machine Learning Research. PMLR; 2017. p. 1321–1330. Kishi et al. [2022] Kishi M, Hirai F, Takatsu N, et al. A review on the current status and definitions of activity indices in inflammatory bowel disease: how to use indices for precise evaluation. Journal of Gastroenterology. 2022;14:246–266. Woo S, Debnath S, Hu R, Chen X, Liu Z, Kweon IS, et al. ConvNeXt V2: Co-designing and Scaling ConvNets with Masked Autoencoders. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR); 2023. p. 6133–16142. Deng et al. [2009] Deng J, Dong W, Socher R, Li LJ, Li K, Fei-Fei L. Imagenet: A large-scale hierarchical image database. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR); 2009. p. 248–255. Guo et al. [2017] Guo C, Pleiss G, Sun Y, Weinberger KQ. On Calibration of Modern Neural Networks. In: Precup O, Teh YW, editors. Proceedings of the 34th International Conference on Machine Learning. vol. 70 of Proceedings of Machine Learning Research. PMLR; 2017. p. 1321–1330. Kishi et al. [2022] Kishi M, Hirai F, Takatsu N, et al. A review on the current status and definitions of activity indices in inflammatory bowel disease: how to use indices for precise evaluation. Journal of Gastroenterology. 2022;14:246–266. Deng J, Dong W, Socher R, Li LJ, Li K, Fei-Fei L. Imagenet: A large-scale hierarchical image database. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR); 2009. p. 248–255. Guo et al. [2017] Guo C, Pleiss G, Sun Y, Weinberger KQ. On Calibration of Modern Neural Networks. In: Precup O, Teh YW, editors. Proceedings of the 34th International Conference on Machine Learning. vol. 70 of Proceedings of Machine Learning Research. PMLR; 2017. p. 1321–1330. Kishi et al. [2022] Kishi M, Hirai F, Takatsu N, et al. A review on the current status and definitions of activity indices in inflammatory bowel disease: how to use indices for precise evaluation. Journal of Gastroenterology. 2022;14:246–266. Guo C, Pleiss G, Sun Y, Weinberger KQ. On Calibration of Modern Neural Networks. In: Precup O, Teh YW, editors. Proceedings of the 34th International Conference on Machine Learning. vol. 70 of Proceedings of Machine Learning Research. PMLR; 2017. p. 1321–1330. Kishi et al. [2022] Kishi M, Hirai F, Takatsu N, et al. A review on the current status and definitions of activity indices in inflammatory bowel disease: how to use indices for precise evaluation. Journal of Gastroenterology. 2022;14:246–266. Kishi M, Hirai F, Takatsu N, et al. A review on the current status and definitions of activity indices in inflammatory bowel disease: how to use indices for precise evaluation. Journal of Gastroenterology. 2022;14:246–266.
- Global, regional and national burden of inflammatory bowel disease in 204 countries and territories from 1990 to 2019: a systematic analysis based on the Global Burden of Disease Study 2019. BMJ Open. 2023;13(3). 10.1136/bmjopen-2022-065186. Preisler et al. [2015] Preisler L, Svendsen M, Nerup N, Svendsen L, Konge L. Simulation-Based Training for Colonoscopy Establishing Criteria for Competency. Medicine. 2015;94(4):e440. Schroeder et al. [1987] Schroeder KW, Tremaine WJ, Ilstrup DM. Coated Oral 5-Aminosalicylic Acid Therapy for Mildly to Moderately Active Ulcerative Colitis. New England Journal of Medicine. 1987;317(26):1625–1629. PMID: 3317057. Samaan et al. [2014] Samaan MA, Mosli MH, Sandborn WJ, Feagan BG, D’Haens GR, Dubcenco E, et al. A Systematic Review of the Measurement of Endoscopic Healing in Ulcerative Colitis Clinical Trials: Recommendations and Implications for Future Research. Inflammatory Bowel Diseases. 2014 05;20(8):1465–1471. Vashist et al. [2018] Vashist N, Samaan M, Mosli M, Parker C, MacDonald J, Nelson S, et al. Endoscopic scoring indices for evaluation of disease activity in ulcerative colitis. Cohrane Database of Systematic Reviews. 2018;1(1):CD011450. Ali [2022] Ali S. Where do we stand in AI for endoscopic image analysis? Deciphering gaps and future directions. npj Digital Medicine. 2022;5(1):184. Lo and Burisch [2021] Lo B, Burisch J. Artificial intelligence assisted assessment of endoscopic disease activity in inflammatory bowel disease. Artificial Intelligence in Gastrointestinal Endoscopy. 2021;2(4):95–102. Lo et al. [2022] Lo B, Liu Z, Bendtsen F, Igel C, Vind I, Burisch J. High accuracy in classifying endoscopic severity in ulcerative colitis using convolutional neural network. The American Journal of Gastroenterology. 2022;117(11):1648–1654. Becker et al. [2021] Becker BG, Arcadu F, Thalhammer A, Serna CG, Feehan O, Drawnel F, et al. Training and deploying a deep learning model for endoscopic severity grading in ulcerative colitis using multicenter clinical trial data. Therapeutic Advances in Gastrointestinal Endoscopy. 2021;14:2631774521990623. Lo et al. [2021] Lo B, Liu Z, Bendtsen F, Igel C, Vind I, Burisch TPP Johan. OP07 Artificial intelligence surpasses gastrointestinal experts in the classification of endoscopic severity among Ulcerative Colitis. Journal of Crohn’s and Colitis. 2021 05;15(Supplement):S007–S007. Vaze et al. [2022] Vaze S, Han K, Vedaldi A, Zisserman A. Open-set recognition: A good closed-set classifier is all you need? In: International Conference on Learning Representations (ICLR); 2022. . Gottlieb et al. [2021] Gottlieb K, Requa J, Karnes W, Chandra Gudivada R, Shen J, Rael E, et al. Central Reading of Ulcerative Colitis Clinical Trial Videos Using Neural Networks. Gastroenterology. 2021;160(3):710–719.e2. Woo et al. [2023] Woo S, Debnath S, Hu R, Chen X, Liu Z, Kweon IS, et al. ConvNeXt V2: Co-designing and Scaling ConvNets with Masked Autoencoders. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR); 2023. p. 6133–16142. Deng et al. [2009] Deng J, Dong W, Socher R, Li LJ, Li K, Fei-Fei L. Imagenet: A large-scale hierarchical image database. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR); 2009. p. 248–255. Guo et al. [2017] Guo C, Pleiss G, Sun Y, Weinberger KQ. On Calibration of Modern Neural Networks. In: Precup O, Teh YW, editors. Proceedings of the 34th International Conference on Machine Learning. vol. 70 of Proceedings of Machine Learning Research. PMLR; 2017. p. 1321–1330. Kishi et al. [2022] Kishi M, Hirai F, Takatsu N, et al. A review on the current status and definitions of activity indices in inflammatory bowel disease: how to use indices for precise evaluation. Journal of Gastroenterology. 2022;14:246–266. Preisler L, Svendsen M, Nerup N, Svendsen L, Konge L. Simulation-Based Training for Colonoscopy Establishing Criteria for Competency. Medicine. 2015;94(4):e440. Schroeder et al. [1987] Schroeder KW, Tremaine WJ, Ilstrup DM. Coated Oral 5-Aminosalicylic Acid Therapy for Mildly to Moderately Active Ulcerative Colitis. New England Journal of Medicine. 1987;317(26):1625–1629. PMID: 3317057. Samaan et al. [2014] Samaan MA, Mosli MH, Sandborn WJ, Feagan BG, D’Haens GR, Dubcenco E, et al. A Systematic Review of the Measurement of Endoscopic Healing in Ulcerative Colitis Clinical Trials: Recommendations and Implications for Future Research. Inflammatory Bowel Diseases. 2014 05;20(8):1465–1471. Vashist et al. [2018] Vashist N, Samaan M, Mosli M, Parker C, MacDonald J, Nelson S, et al. Endoscopic scoring indices for evaluation of disease activity in ulcerative colitis. Cohrane Database of Systematic Reviews. 2018;1(1):CD011450. Ali [2022] Ali S. Where do we stand in AI for endoscopic image analysis? Deciphering gaps and future directions. npj Digital Medicine. 2022;5(1):184. Lo and Burisch [2021] Lo B, Burisch J. Artificial intelligence assisted assessment of endoscopic disease activity in inflammatory bowel disease. Artificial Intelligence in Gastrointestinal Endoscopy. 2021;2(4):95–102. Lo et al. [2022] Lo B, Liu Z, Bendtsen F, Igel C, Vind I, Burisch J. High accuracy in classifying endoscopic severity in ulcerative colitis using convolutional neural network. The American Journal of Gastroenterology. 2022;117(11):1648–1654. Becker et al. [2021] Becker BG, Arcadu F, Thalhammer A, Serna CG, Feehan O, Drawnel F, et al. Training and deploying a deep learning model for endoscopic severity grading in ulcerative colitis using multicenter clinical trial data. Therapeutic Advances in Gastrointestinal Endoscopy. 2021;14:2631774521990623. Lo et al. [2021] Lo B, Liu Z, Bendtsen F, Igel C, Vind I, Burisch TPP Johan. OP07 Artificial intelligence surpasses gastrointestinal experts in the classification of endoscopic severity among Ulcerative Colitis. Journal of Crohn’s and Colitis. 2021 05;15(Supplement):S007–S007. Vaze et al. [2022] Vaze S, Han K, Vedaldi A, Zisserman A. Open-set recognition: A good closed-set classifier is all you need? In: International Conference on Learning Representations (ICLR); 2022. . Gottlieb et al. [2021] Gottlieb K, Requa J, Karnes W, Chandra Gudivada R, Shen J, Rael E, et al. Central Reading of Ulcerative Colitis Clinical Trial Videos Using Neural Networks. Gastroenterology. 2021;160(3):710–719.e2. Woo et al. [2023] Woo S, Debnath S, Hu R, Chen X, Liu Z, Kweon IS, et al. ConvNeXt V2: Co-designing and Scaling ConvNets with Masked Autoencoders. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR); 2023. p. 6133–16142. Deng et al. [2009] Deng J, Dong W, Socher R, Li LJ, Li K, Fei-Fei L. Imagenet: A large-scale hierarchical image database. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR); 2009. p. 248–255. Guo et al. [2017] Guo C, Pleiss G, Sun Y, Weinberger KQ. On Calibration of Modern Neural Networks. In: Precup O, Teh YW, editors. Proceedings of the 34th International Conference on Machine Learning. vol. 70 of Proceedings of Machine Learning Research. PMLR; 2017. p. 1321–1330. Kishi et al. [2022] Kishi M, Hirai F, Takatsu N, et al. A review on the current status and definitions of activity indices in inflammatory bowel disease: how to use indices for precise evaluation. Journal of Gastroenterology. 2022;14:246–266. Schroeder KW, Tremaine WJ, Ilstrup DM. Coated Oral 5-Aminosalicylic Acid Therapy for Mildly to Moderately Active Ulcerative Colitis. New England Journal of Medicine. 1987;317(26):1625–1629. PMID: 3317057. Samaan et al. [2014] Samaan MA, Mosli MH, Sandborn WJ, Feagan BG, D’Haens GR, Dubcenco E, et al. A Systematic Review of the Measurement of Endoscopic Healing in Ulcerative Colitis Clinical Trials: Recommendations and Implications for Future Research. Inflammatory Bowel Diseases. 2014 05;20(8):1465–1471. Vashist et al. [2018] Vashist N, Samaan M, Mosli M, Parker C, MacDonald J, Nelson S, et al. Endoscopic scoring indices for evaluation of disease activity in ulcerative colitis. Cohrane Database of Systematic Reviews. 2018;1(1):CD011450. Ali [2022] Ali S. Where do we stand in AI for endoscopic image analysis? Deciphering gaps and future directions. npj Digital Medicine. 2022;5(1):184. Lo and Burisch [2021] Lo B, Burisch J. Artificial intelligence assisted assessment of endoscopic disease activity in inflammatory bowel disease. Artificial Intelligence in Gastrointestinal Endoscopy. 2021;2(4):95–102. Lo et al. [2022] Lo B, Liu Z, Bendtsen F, Igel C, Vind I, Burisch J. High accuracy in classifying endoscopic severity in ulcerative colitis using convolutional neural network. The American Journal of Gastroenterology. 2022;117(11):1648–1654. Becker et al. [2021] Becker BG, Arcadu F, Thalhammer A, Serna CG, Feehan O, Drawnel F, et al. Training and deploying a deep learning model for endoscopic severity grading in ulcerative colitis using multicenter clinical trial data. Therapeutic Advances in Gastrointestinal Endoscopy. 2021;14:2631774521990623. Lo et al. [2021] Lo B, Liu Z, Bendtsen F, Igel C, Vind I, Burisch TPP Johan. OP07 Artificial intelligence surpasses gastrointestinal experts in the classification of endoscopic severity among Ulcerative Colitis. Journal of Crohn’s and Colitis. 2021 05;15(Supplement):S007–S007. Vaze et al. [2022] Vaze S, Han K, Vedaldi A, Zisserman A. Open-set recognition: A good closed-set classifier is all you need? In: International Conference on Learning Representations (ICLR); 2022. . Gottlieb et al. [2021] Gottlieb K, Requa J, Karnes W, Chandra Gudivada R, Shen J, Rael E, et al. Central Reading of Ulcerative Colitis Clinical Trial Videos Using Neural Networks. Gastroenterology. 2021;160(3):710–719.e2. Woo et al. [2023] Woo S, Debnath S, Hu R, Chen X, Liu Z, Kweon IS, et al. ConvNeXt V2: Co-designing and Scaling ConvNets with Masked Autoencoders. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR); 2023. p. 6133–16142. Deng et al. [2009] Deng J, Dong W, Socher R, Li LJ, Li K, Fei-Fei L. Imagenet: A large-scale hierarchical image database. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR); 2009. p. 248–255. Guo et al. [2017] Guo C, Pleiss G, Sun Y, Weinberger KQ. On Calibration of Modern Neural Networks. In: Precup O, Teh YW, editors. Proceedings of the 34th International Conference on Machine Learning. vol. 70 of Proceedings of Machine Learning Research. PMLR; 2017. p. 1321–1330. Kishi et al. [2022] Kishi M, Hirai F, Takatsu N, et al. A review on the current status and definitions of activity indices in inflammatory bowel disease: how to use indices for precise evaluation. Journal of Gastroenterology. 2022;14:246–266. Samaan MA, Mosli MH, Sandborn WJ, Feagan BG, D’Haens GR, Dubcenco E, et al. A Systematic Review of the Measurement of Endoscopic Healing in Ulcerative Colitis Clinical Trials: Recommendations and Implications for Future Research. Inflammatory Bowel Diseases. 2014 05;20(8):1465–1471. Vashist et al. [2018] Vashist N, Samaan M, Mosli M, Parker C, MacDonald J, Nelson S, et al. Endoscopic scoring indices for evaluation of disease activity in ulcerative colitis. Cohrane Database of Systematic Reviews. 2018;1(1):CD011450. Ali [2022] Ali S. Where do we stand in AI for endoscopic image analysis? Deciphering gaps and future directions. npj Digital Medicine. 2022;5(1):184. Lo and Burisch [2021] Lo B, Burisch J. Artificial intelligence assisted assessment of endoscopic disease activity in inflammatory bowel disease. Artificial Intelligence in Gastrointestinal Endoscopy. 2021;2(4):95–102. Lo et al. [2022] Lo B, Liu Z, Bendtsen F, Igel C, Vind I, Burisch J. High accuracy in classifying endoscopic severity in ulcerative colitis using convolutional neural network. The American Journal of Gastroenterology. 2022;117(11):1648–1654. Becker et al. [2021] Becker BG, Arcadu F, Thalhammer A, Serna CG, Feehan O, Drawnel F, et al. Training and deploying a deep learning model for endoscopic severity grading in ulcerative colitis using multicenter clinical trial data. Therapeutic Advances in Gastrointestinal Endoscopy. 2021;14:2631774521990623. Lo et al. [2021] Lo B, Liu Z, Bendtsen F, Igel C, Vind I, Burisch TPP Johan. OP07 Artificial intelligence surpasses gastrointestinal experts in the classification of endoscopic severity among Ulcerative Colitis. Journal of Crohn’s and Colitis. 2021 05;15(Supplement):S007–S007. Vaze et al. [2022] Vaze S, Han K, Vedaldi A, Zisserman A. Open-set recognition: A good closed-set classifier is all you need? In: International Conference on Learning Representations (ICLR); 2022. . Gottlieb et al. [2021] Gottlieb K, Requa J, Karnes W, Chandra Gudivada R, Shen J, Rael E, et al. Central Reading of Ulcerative Colitis Clinical Trial Videos Using Neural Networks. Gastroenterology. 2021;160(3):710–719.e2. Woo et al. [2023] Woo S, Debnath S, Hu R, Chen X, Liu Z, Kweon IS, et al. ConvNeXt V2: Co-designing and Scaling ConvNets with Masked Autoencoders. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR); 2023. p. 6133–16142. Deng et al. [2009] Deng J, Dong W, Socher R, Li LJ, Li K, Fei-Fei L. Imagenet: A large-scale hierarchical image database. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR); 2009. p. 248–255. Guo et al. [2017] Guo C, Pleiss G, Sun Y, Weinberger KQ. On Calibration of Modern Neural Networks. In: Precup O, Teh YW, editors. Proceedings of the 34th International Conference on Machine Learning. vol. 70 of Proceedings of Machine Learning Research. PMLR; 2017. p. 1321–1330. Kishi et al. [2022] Kishi M, Hirai F, Takatsu N, et al. A review on the current status and definitions of activity indices in inflammatory bowel disease: how to use indices for precise evaluation. Journal of Gastroenterology. 2022;14:246–266. Vashist N, Samaan M, Mosli M, Parker C, MacDonald J, Nelson S, et al. Endoscopic scoring indices for evaluation of disease activity in ulcerative colitis. Cohrane Database of Systematic Reviews. 2018;1(1):CD011450. Ali [2022] Ali S. Where do we stand in AI for endoscopic image analysis? Deciphering gaps and future directions. npj Digital Medicine. 2022;5(1):184. Lo and Burisch [2021] Lo B, Burisch J. Artificial intelligence assisted assessment of endoscopic disease activity in inflammatory bowel disease. Artificial Intelligence in Gastrointestinal Endoscopy. 2021;2(4):95–102. Lo et al. [2022] Lo B, Liu Z, Bendtsen F, Igel C, Vind I, Burisch J. High accuracy in classifying endoscopic severity in ulcerative colitis using convolutional neural network. The American Journal of Gastroenterology. 2022;117(11):1648–1654. Becker et al. [2021] Becker BG, Arcadu F, Thalhammer A, Serna CG, Feehan O, Drawnel F, et al. Training and deploying a deep learning model for endoscopic severity grading in ulcerative colitis using multicenter clinical trial data. Therapeutic Advances in Gastrointestinal Endoscopy. 2021;14:2631774521990623. Lo et al. [2021] Lo B, Liu Z, Bendtsen F, Igel C, Vind I, Burisch TPP Johan. OP07 Artificial intelligence surpasses gastrointestinal experts in the classification of endoscopic severity among Ulcerative Colitis. Journal of Crohn’s and Colitis. 2021 05;15(Supplement):S007–S007. Vaze et al. [2022] Vaze S, Han K, Vedaldi A, Zisserman A. Open-set recognition: A good closed-set classifier is all you need? In: International Conference on Learning Representations (ICLR); 2022. . Gottlieb et al. [2021] Gottlieb K, Requa J, Karnes W, Chandra Gudivada R, Shen J, Rael E, et al. Central Reading of Ulcerative Colitis Clinical Trial Videos Using Neural Networks. Gastroenterology. 2021;160(3):710–719.e2. Woo et al. [2023] Woo S, Debnath S, Hu R, Chen X, Liu Z, Kweon IS, et al. ConvNeXt V2: Co-designing and Scaling ConvNets with Masked Autoencoders. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR); 2023. p. 6133–16142. Deng et al. [2009] Deng J, Dong W, Socher R, Li LJ, Li K, Fei-Fei L. Imagenet: A large-scale hierarchical image database. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR); 2009. p. 248–255. Guo et al. [2017] Guo C, Pleiss G, Sun Y, Weinberger KQ. On Calibration of Modern Neural Networks. In: Precup O, Teh YW, editors. Proceedings of the 34th International Conference on Machine Learning. vol. 70 of Proceedings of Machine Learning Research. PMLR; 2017. p. 1321–1330. Kishi et al. [2022] Kishi M, Hirai F, Takatsu N, et al. A review on the current status and definitions of activity indices in inflammatory bowel disease: how to use indices for precise evaluation. Journal of Gastroenterology. 2022;14:246–266. Ali S. Where do we stand in AI for endoscopic image analysis? Deciphering gaps and future directions. npj Digital Medicine. 2022;5(1):184. Lo and Burisch [2021] Lo B, Burisch J. Artificial intelligence assisted assessment of endoscopic disease activity in inflammatory bowel disease. Artificial Intelligence in Gastrointestinal Endoscopy. 2021;2(4):95–102. Lo et al. [2022] Lo B, Liu Z, Bendtsen F, Igel C, Vind I, Burisch J. High accuracy in classifying endoscopic severity in ulcerative colitis using convolutional neural network. The American Journal of Gastroenterology. 2022;117(11):1648–1654. Becker et al. [2021] Becker BG, Arcadu F, Thalhammer A, Serna CG, Feehan O, Drawnel F, et al. Training and deploying a deep learning model for endoscopic severity grading in ulcerative colitis using multicenter clinical trial data. Therapeutic Advances in Gastrointestinal Endoscopy. 2021;14:2631774521990623. Lo et al. [2021] Lo B, Liu Z, Bendtsen F, Igel C, Vind I, Burisch TPP Johan. OP07 Artificial intelligence surpasses gastrointestinal experts in the classification of endoscopic severity among Ulcerative Colitis. Journal of Crohn’s and Colitis. 2021 05;15(Supplement):S007–S007. Vaze et al. [2022] Vaze S, Han K, Vedaldi A, Zisserman A. Open-set recognition: A good closed-set classifier is all you need? In: International Conference on Learning Representations (ICLR); 2022. . Gottlieb et al. [2021] Gottlieb K, Requa J, Karnes W, Chandra Gudivada R, Shen J, Rael E, et al. Central Reading of Ulcerative Colitis Clinical Trial Videos Using Neural Networks. Gastroenterology. 2021;160(3):710–719.e2. Woo et al. [2023] Woo S, Debnath S, Hu R, Chen X, Liu Z, Kweon IS, et al. ConvNeXt V2: Co-designing and Scaling ConvNets with Masked Autoencoders. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR); 2023. p. 6133–16142. Deng et al. [2009] Deng J, Dong W, Socher R, Li LJ, Li K, Fei-Fei L. Imagenet: A large-scale hierarchical image database. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR); 2009. p. 248–255. Guo et al. [2017] Guo C, Pleiss G, Sun Y, Weinberger KQ. On Calibration of Modern Neural Networks. In: Precup O, Teh YW, editors. Proceedings of the 34th International Conference on Machine Learning. vol. 70 of Proceedings of Machine Learning Research. PMLR; 2017. p. 1321–1330. Kishi et al. [2022] Kishi M, Hirai F, Takatsu N, et al. A review on the current status and definitions of activity indices in inflammatory bowel disease: how to use indices for precise evaluation. Journal of Gastroenterology. 2022;14:246–266. Lo B, Burisch J. Artificial intelligence assisted assessment of endoscopic disease activity in inflammatory bowel disease. Artificial Intelligence in Gastrointestinal Endoscopy. 2021;2(4):95–102. Lo et al. [2022] Lo B, Liu Z, Bendtsen F, Igel C, Vind I, Burisch J. High accuracy in classifying endoscopic severity in ulcerative colitis using convolutional neural network. The American Journal of Gastroenterology. 2022;117(11):1648–1654. Becker et al. [2021] Becker BG, Arcadu F, Thalhammer A, Serna CG, Feehan O, Drawnel F, et al. Training and deploying a deep learning model for endoscopic severity grading in ulcerative colitis using multicenter clinical trial data. Therapeutic Advances in Gastrointestinal Endoscopy. 2021;14:2631774521990623. Lo et al. [2021] Lo B, Liu Z, Bendtsen F, Igel C, Vind I, Burisch TPP Johan. OP07 Artificial intelligence surpasses gastrointestinal experts in the classification of endoscopic severity among Ulcerative Colitis. Journal of Crohn’s and Colitis. 2021 05;15(Supplement):S007–S007. Vaze et al. [2022] Vaze S, Han K, Vedaldi A, Zisserman A. Open-set recognition: A good closed-set classifier is all you need? In: International Conference on Learning Representations (ICLR); 2022. . Gottlieb et al. [2021] Gottlieb K, Requa J, Karnes W, Chandra Gudivada R, Shen J, Rael E, et al. Central Reading of Ulcerative Colitis Clinical Trial Videos Using Neural Networks. Gastroenterology. 2021;160(3):710–719.e2. Woo et al. [2023] Woo S, Debnath S, Hu R, Chen X, Liu Z, Kweon IS, et al. ConvNeXt V2: Co-designing and Scaling ConvNets with Masked Autoencoders. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR); 2023. p. 6133–16142. Deng et al. [2009] Deng J, Dong W, Socher R, Li LJ, Li K, Fei-Fei L. Imagenet: A large-scale hierarchical image database. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR); 2009. p. 248–255. Guo et al. [2017] Guo C, Pleiss G, Sun Y, Weinberger KQ. On Calibration of Modern Neural Networks. In: Precup O, Teh YW, editors. Proceedings of the 34th International Conference on Machine Learning. vol. 70 of Proceedings of Machine Learning Research. PMLR; 2017. p. 1321–1330. Kishi et al. [2022] Kishi M, Hirai F, Takatsu N, et al. A review on the current status and definitions of activity indices in inflammatory bowel disease: how to use indices for precise evaluation. Journal of Gastroenterology. 2022;14:246–266. Lo B, Liu Z, Bendtsen F, Igel C, Vind I, Burisch J. High accuracy in classifying endoscopic severity in ulcerative colitis using convolutional neural network. The American Journal of Gastroenterology. 2022;117(11):1648–1654. Becker et al. [2021] Becker BG, Arcadu F, Thalhammer A, Serna CG, Feehan O, Drawnel F, et al. Training and deploying a deep learning model for endoscopic severity grading in ulcerative colitis using multicenter clinical trial data. Therapeutic Advances in Gastrointestinal Endoscopy. 2021;14:2631774521990623. Lo et al. [2021] Lo B, Liu Z, Bendtsen F, Igel C, Vind I, Burisch TPP Johan. OP07 Artificial intelligence surpasses gastrointestinal experts in the classification of endoscopic severity among Ulcerative Colitis. Journal of Crohn’s and Colitis. 2021 05;15(Supplement):S007–S007. Vaze et al. [2022] Vaze S, Han K, Vedaldi A, Zisserman A. Open-set recognition: A good closed-set classifier is all you need? In: International Conference on Learning Representations (ICLR); 2022. . Gottlieb et al. [2021] Gottlieb K, Requa J, Karnes W, Chandra Gudivada R, Shen J, Rael E, et al. Central Reading of Ulcerative Colitis Clinical Trial Videos Using Neural Networks. Gastroenterology. 2021;160(3):710–719.e2. Woo et al. [2023] Woo S, Debnath S, Hu R, Chen X, Liu Z, Kweon IS, et al. ConvNeXt V2: Co-designing and Scaling ConvNets with Masked Autoencoders. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR); 2023. p. 6133–16142. Deng et al. [2009] Deng J, Dong W, Socher R, Li LJ, Li K, Fei-Fei L. Imagenet: A large-scale hierarchical image database. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR); 2009. p. 248–255. Guo et al. [2017] Guo C, Pleiss G, Sun Y, Weinberger KQ. On Calibration of Modern Neural Networks. In: Precup O, Teh YW, editors. Proceedings of the 34th International Conference on Machine Learning. vol. 70 of Proceedings of Machine Learning Research. PMLR; 2017. p. 1321–1330. Kishi et al. [2022] Kishi M, Hirai F, Takatsu N, et al. A review on the current status and definitions of activity indices in inflammatory bowel disease: how to use indices for precise evaluation. Journal of Gastroenterology. 2022;14:246–266. Becker BG, Arcadu F, Thalhammer A, Serna CG, Feehan O, Drawnel F, et al. Training and deploying a deep learning model for endoscopic severity grading in ulcerative colitis using multicenter clinical trial data. Therapeutic Advances in Gastrointestinal Endoscopy. 2021;14:2631774521990623. Lo et al. [2021] Lo B, Liu Z, Bendtsen F, Igel C, Vind I, Burisch TPP Johan. OP07 Artificial intelligence surpasses gastrointestinal experts in the classification of endoscopic severity among Ulcerative Colitis. Journal of Crohn’s and Colitis. 2021 05;15(Supplement):S007–S007. Vaze et al. [2022] Vaze S, Han K, Vedaldi A, Zisserman A. Open-set recognition: A good closed-set classifier is all you need? In: International Conference on Learning Representations (ICLR); 2022. . Gottlieb et al. [2021] Gottlieb K, Requa J, Karnes W, Chandra Gudivada R, Shen J, Rael E, et al. Central Reading of Ulcerative Colitis Clinical Trial Videos Using Neural Networks. Gastroenterology. 2021;160(3):710–719.e2. Woo et al. [2023] Woo S, Debnath S, Hu R, Chen X, Liu Z, Kweon IS, et al. ConvNeXt V2: Co-designing and Scaling ConvNets with Masked Autoencoders. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR); 2023. p. 6133–16142. Deng et al. [2009] Deng J, Dong W, Socher R, Li LJ, Li K, Fei-Fei L. Imagenet: A large-scale hierarchical image database. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR); 2009. p. 248–255. Guo et al. [2017] Guo C, Pleiss G, Sun Y, Weinberger KQ. On Calibration of Modern Neural Networks. In: Precup O, Teh YW, editors. Proceedings of the 34th International Conference on Machine Learning. vol. 70 of Proceedings of Machine Learning Research. PMLR; 2017. p. 1321–1330. Kishi et al. [2022] Kishi M, Hirai F, Takatsu N, et al. A review on the current status and definitions of activity indices in inflammatory bowel disease: how to use indices for precise evaluation. Journal of Gastroenterology. 2022;14:246–266. Lo B, Liu Z, Bendtsen F, Igel C, Vind I, Burisch TPP Johan. OP07 Artificial intelligence surpasses gastrointestinal experts in the classification of endoscopic severity among Ulcerative Colitis. Journal of Crohn’s and Colitis. 2021 05;15(Supplement):S007–S007. Vaze et al. [2022] Vaze S, Han K, Vedaldi A, Zisserman A. Open-set recognition: A good closed-set classifier is all you need? In: International Conference on Learning Representations (ICLR); 2022. . Gottlieb et al. [2021] Gottlieb K, Requa J, Karnes W, Chandra Gudivada R, Shen J, Rael E, et al. Central Reading of Ulcerative Colitis Clinical Trial Videos Using Neural Networks. Gastroenterology. 2021;160(3):710–719.e2. Woo et al. [2023] Woo S, Debnath S, Hu R, Chen X, Liu Z, Kweon IS, et al. ConvNeXt V2: Co-designing and Scaling ConvNets with Masked Autoencoders. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR); 2023. p. 6133–16142. Deng et al. [2009] Deng J, Dong W, Socher R, Li LJ, Li K, Fei-Fei L. Imagenet: A large-scale hierarchical image database. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR); 2009. p. 248–255. Guo et al. [2017] Guo C, Pleiss G, Sun Y, Weinberger KQ. On Calibration of Modern Neural Networks. In: Precup O, Teh YW, editors. Proceedings of the 34th International Conference on Machine Learning. vol. 70 of Proceedings of Machine Learning Research. PMLR; 2017. p. 1321–1330. Kishi et al. [2022] Kishi M, Hirai F, Takatsu N, et al. A review on the current status and definitions of activity indices in inflammatory bowel disease: how to use indices for precise evaluation. Journal of Gastroenterology. 2022;14:246–266. Vaze S, Han K, Vedaldi A, Zisserman A. Open-set recognition: A good closed-set classifier is all you need? In: International Conference on Learning Representations (ICLR); 2022. . Gottlieb et al. [2021] Gottlieb K, Requa J, Karnes W, Chandra Gudivada R, Shen J, Rael E, et al. Central Reading of Ulcerative Colitis Clinical Trial Videos Using Neural Networks. Gastroenterology. 2021;160(3):710–719.e2. Woo et al. [2023] Woo S, Debnath S, Hu R, Chen X, Liu Z, Kweon IS, et al. ConvNeXt V2: Co-designing and Scaling ConvNets with Masked Autoencoders. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR); 2023. p. 6133–16142. Deng et al. [2009] Deng J, Dong W, Socher R, Li LJ, Li K, Fei-Fei L. Imagenet: A large-scale hierarchical image database. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR); 2009. p. 248–255. Guo et al. [2017] Guo C, Pleiss G, Sun Y, Weinberger KQ. On Calibration of Modern Neural Networks. In: Precup O, Teh YW, editors. Proceedings of the 34th International Conference on Machine Learning. vol. 70 of Proceedings of Machine Learning Research. PMLR; 2017. p. 1321–1330. Kishi et al. [2022] Kishi M, Hirai F, Takatsu N, et al. A review on the current status and definitions of activity indices in inflammatory bowel disease: how to use indices for precise evaluation. Journal of Gastroenterology. 2022;14:246–266. Gottlieb K, Requa J, Karnes W, Chandra Gudivada R, Shen J, Rael E, et al. Central Reading of Ulcerative Colitis Clinical Trial Videos Using Neural Networks. Gastroenterology. 2021;160(3):710–719.e2. Woo et al. [2023] Woo S, Debnath S, Hu R, Chen X, Liu Z, Kweon IS, et al. ConvNeXt V2: Co-designing and Scaling ConvNets with Masked Autoencoders. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR); 2023. p. 6133–16142. Deng et al. [2009] Deng J, Dong W, Socher R, Li LJ, Li K, Fei-Fei L. Imagenet: A large-scale hierarchical image database. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR); 2009. p. 248–255. Guo et al. [2017] Guo C, Pleiss G, Sun Y, Weinberger KQ. On Calibration of Modern Neural Networks. In: Precup O, Teh YW, editors. Proceedings of the 34th International Conference on Machine Learning. vol. 70 of Proceedings of Machine Learning Research. PMLR; 2017. p. 1321–1330. Kishi et al. [2022] Kishi M, Hirai F, Takatsu N, et al. A review on the current status and definitions of activity indices in inflammatory bowel disease: how to use indices for precise evaluation. Journal of Gastroenterology. 2022;14:246–266. Woo S, Debnath S, Hu R, Chen X, Liu Z, Kweon IS, et al. ConvNeXt V2: Co-designing and Scaling ConvNets with Masked Autoencoders. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR); 2023. p. 6133–16142. Deng et al. [2009] Deng J, Dong W, Socher R, Li LJ, Li K, Fei-Fei L. Imagenet: A large-scale hierarchical image database. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR); 2009. p. 248–255. Guo et al. [2017] Guo C, Pleiss G, Sun Y, Weinberger KQ. On Calibration of Modern Neural Networks. In: Precup O, Teh YW, editors. Proceedings of the 34th International Conference on Machine Learning. vol. 70 of Proceedings of Machine Learning Research. PMLR; 2017. p. 1321–1330. Kishi et al. [2022] Kishi M, Hirai F, Takatsu N, et al. A review on the current status and definitions of activity indices in inflammatory bowel disease: how to use indices for precise evaluation. Journal of Gastroenterology. 2022;14:246–266. Deng J, Dong W, Socher R, Li LJ, Li K, Fei-Fei L. Imagenet: A large-scale hierarchical image database. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR); 2009. p. 248–255. Guo et al. [2017] Guo C, Pleiss G, Sun Y, Weinberger KQ. On Calibration of Modern Neural Networks. In: Precup O, Teh YW, editors. Proceedings of the 34th International Conference on Machine Learning. vol. 70 of Proceedings of Machine Learning Research. PMLR; 2017. p. 1321–1330. Kishi et al. [2022] Kishi M, Hirai F, Takatsu N, et al. A review on the current status and definitions of activity indices in inflammatory bowel disease: how to use indices for precise evaluation. Journal of Gastroenterology. 2022;14:246–266. Guo C, Pleiss G, Sun Y, Weinberger KQ. On Calibration of Modern Neural Networks. In: Precup O, Teh YW, editors. Proceedings of the 34th International Conference on Machine Learning. vol. 70 of Proceedings of Machine Learning Research. PMLR; 2017. p. 1321–1330. Kishi et al. [2022] Kishi M, Hirai F, Takatsu N, et al. A review on the current status and definitions of activity indices in inflammatory bowel disease: how to use indices for precise evaluation. Journal of Gastroenterology. 2022;14:246–266. Kishi M, Hirai F, Takatsu N, et al. A review on the current status and definitions of activity indices in inflammatory bowel disease: how to use indices for precise evaluation. Journal of Gastroenterology. 2022;14:246–266.
- Simulation-Based Training for Colonoscopy Establishing Criteria for Competency. Medicine. 2015;94(4):e440. Schroeder et al. [1987] Schroeder KW, Tremaine WJ, Ilstrup DM. Coated Oral 5-Aminosalicylic Acid Therapy for Mildly to Moderately Active Ulcerative Colitis. New England Journal of Medicine. 1987;317(26):1625–1629. PMID: 3317057. Samaan et al. [2014] Samaan MA, Mosli MH, Sandborn WJ, Feagan BG, D’Haens GR, Dubcenco E, et al. A Systematic Review of the Measurement of Endoscopic Healing in Ulcerative Colitis Clinical Trials: Recommendations and Implications for Future Research. Inflammatory Bowel Diseases. 2014 05;20(8):1465–1471. Vashist et al. [2018] Vashist N, Samaan M, Mosli M, Parker C, MacDonald J, Nelson S, et al. Endoscopic scoring indices for evaluation of disease activity in ulcerative colitis. Cohrane Database of Systematic Reviews. 2018;1(1):CD011450. Ali [2022] Ali S. Where do we stand in AI for endoscopic image analysis? Deciphering gaps and future directions. npj Digital Medicine. 2022;5(1):184. Lo and Burisch [2021] Lo B, Burisch J. Artificial intelligence assisted assessment of endoscopic disease activity in inflammatory bowel disease. Artificial Intelligence in Gastrointestinal Endoscopy. 2021;2(4):95–102. Lo et al. [2022] Lo B, Liu Z, Bendtsen F, Igel C, Vind I, Burisch J. High accuracy in classifying endoscopic severity in ulcerative colitis using convolutional neural network. The American Journal of Gastroenterology. 2022;117(11):1648–1654. Becker et al. [2021] Becker BG, Arcadu F, Thalhammer A, Serna CG, Feehan O, Drawnel F, et al. Training and deploying a deep learning model for endoscopic severity grading in ulcerative colitis using multicenter clinical trial data. Therapeutic Advances in Gastrointestinal Endoscopy. 2021;14:2631774521990623. Lo et al. [2021] Lo B, Liu Z, Bendtsen F, Igel C, Vind I, Burisch TPP Johan. OP07 Artificial intelligence surpasses gastrointestinal experts in the classification of endoscopic severity among Ulcerative Colitis. Journal of Crohn’s and Colitis. 2021 05;15(Supplement):S007–S007. Vaze et al. [2022] Vaze S, Han K, Vedaldi A, Zisserman A. Open-set recognition: A good closed-set classifier is all you need? In: International Conference on Learning Representations (ICLR); 2022. . Gottlieb et al. [2021] Gottlieb K, Requa J, Karnes W, Chandra Gudivada R, Shen J, Rael E, et al. Central Reading of Ulcerative Colitis Clinical Trial Videos Using Neural Networks. Gastroenterology. 2021;160(3):710–719.e2. Woo et al. [2023] Woo S, Debnath S, Hu R, Chen X, Liu Z, Kweon IS, et al. ConvNeXt V2: Co-designing and Scaling ConvNets with Masked Autoencoders. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR); 2023. p. 6133–16142. Deng et al. [2009] Deng J, Dong W, Socher R, Li LJ, Li K, Fei-Fei L. Imagenet: A large-scale hierarchical image database. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR); 2009. p. 248–255. Guo et al. [2017] Guo C, Pleiss G, Sun Y, Weinberger KQ. On Calibration of Modern Neural Networks. In: Precup O, Teh YW, editors. Proceedings of the 34th International Conference on Machine Learning. vol. 70 of Proceedings of Machine Learning Research. PMLR; 2017. p. 1321–1330. Kishi et al. [2022] Kishi M, Hirai F, Takatsu N, et al. A review on the current status and definitions of activity indices in inflammatory bowel disease: how to use indices for precise evaluation. Journal of Gastroenterology. 2022;14:246–266. Schroeder KW, Tremaine WJ, Ilstrup DM. Coated Oral 5-Aminosalicylic Acid Therapy for Mildly to Moderately Active Ulcerative Colitis. New England Journal of Medicine. 1987;317(26):1625–1629. PMID: 3317057. Samaan et al. [2014] Samaan MA, Mosli MH, Sandborn WJ, Feagan BG, D’Haens GR, Dubcenco E, et al. A Systematic Review of the Measurement of Endoscopic Healing in Ulcerative Colitis Clinical Trials: Recommendations and Implications for Future Research. Inflammatory Bowel Diseases. 2014 05;20(8):1465–1471. Vashist et al. [2018] Vashist N, Samaan M, Mosli M, Parker C, MacDonald J, Nelson S, et al. Endoscopic scoring indices for evaluation of disease activity in ulcerative colitis. Cohrane Database of Systematic Reviews. 2018;1(1):CD011450. Ali [2022] Ali S. Where do we stand in AI for endoscopic image analysis? Deciphering gaps and future directions. npj Digital Medicine. 2022;5(1):184. Lo and Burisch [2021] Lo B, Burisch J. Artificial intelligence assisted assessment of endoscopic disease activity in inflammatory bowel disease. Artificial Intelligence in Gastrointestinal Endoscopy. 2021;2(4):95–102. Lo et al. [2022] Lo B, Liu Z, Bendtsen F, Igel C, Vind I, Burisch J. High accuracy in classifying endoscopic severity in ulcerative colitis using convolutional neural network. The American Journal of Gastroenterology. 2022;117(11):1648–1654. Becker et al. [2021] Becker BG, Arcadu F, Thalhammer A, Serna CG, Feehan O, Drawnel F, et al. Training and deploying a deep learning model for endoscopic severity grading in ulcerative colitis using multicenter clinical trial data. Therapeutic Advances in Gastrointestinal Endoscopy. 2021;14:2631774521990623. Lo et al. [2021] Lo B, Liu Z, Bendtsen F, Igel C, Vind I, Burisch TPP Johan. OP07 Artificial intelligence surpasses gastrointestinal experts in the classification of endoscopic severity among Ulcerative Colitis. Journal of Crohn’s and Colitis. 2021 05;15(Supplement):S007–S007. Vaze et al. [2022] Vaze S, Han K, Vedaldi A, Zisserman A. Open-set recognition: A good closed-set classifier is all you need? In: International Conference on Learning Representations (ICLR); 2022. . Gottlieb et al. [2021] Gottlieb K, Requa J, Karnes W, Chandra Gudivada R, Shen J, Rael E, et al. Central Reading of Ulcerative Colitis Clinical Trial Videos Using Neural Networks. Gastroenterology. 2021;160(3):710–719.e2. Woo et al. [2023] Woo S, Debnath S, Hu R, Chen X, Liu Z, Kweon IS, et al. ConvNeXt V2: Co-designing and Scaling ConvNets with Masked Autoencoders. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR); 2023. p. 6133–16142. Deng et al. [2009] Deng J, Dong W, Socher R, Li LJ, Li K, Fei-Fei L. Imagenet: A large-scale hierarchical image database. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR); 2009. p. 248–255. Guo et al. [2017] Guo C, Pleiss G, Sun Y, Weinberger KQ. On Calibration of Modern Neural Networks. In: Precup O, Teh YW, editors. Proceedings of the 34th International Conference on Machine Learning. vol. 70 of Proceedings of Machine Learning Research. PMLR; 2017. p. 1321–1330. Kishi et al. [2022] Kishi M, Hirai F, Takatsu N, et al. A review on the current status and definitions of activity indices in inflammatory bowel disease: how to use indices for precise evaluation. Journal of Gastroenterology. 2022;14:246–266. Samaan MA, Mosli MH, Sandborn WJ, Feagan BG, D’Haens GR, Dubcenco E, et al. A Systematic Review of the Measurement of Endoscopic Healing in Ulcerative Colitis Clinical Trials: Recommendations and Implications for Future Research. Inflammatory Bowel Diseases. 2014 05;20(8):1465–1471. Vashist et al. [2018] Vashist N, Samaan M, Mosli M, Parker C, MacDonald J, Nelson S, et al. Endoscopic scoring indices for evaluation of disease activity in ulcerative colitis. Cohrane Database of Systematic Reviews. 2018;1(1):CD011450. Ali [2022] Ali S. Where do we stand in AI for endoscopic image analysis? Deciphering gaps and future directions. npj Digital Medicine. 2022;5(1):184. Lo and Burisch [2021] Lo B, Burisch J. Artificial intelligence assisted assessment of endoscopic disease activity in inflammatory bowel disease. Artificial Intelligence in Gastrointestinal Endoscopy. 2021;2(4):95–102. Lo et al. [2022] Lo B, Liu Z, Bendtsen F, Igel C, Vind I, Burisch J. High accuracy in classifying endoscopic severity in ulcerative colitis using convolutional neural network. The American Journal of Gastroenterology. 2022;117(11):1648–1654. Becker et al. [2021] Becker BG, Arcadu F, Thalhammer A, Serna CG, Feehan O, Drawnel F, et al. Training and deploying a deep learning model for endoscopic severity grading in ulcerative colitis using multicenter clinical trial data. Therapeutic Advances in Gastrointestinal Endoscopy. 2021;14:2631774521990623. Lo et al. [2021] Lo B, Liu Z, Bendtsen F, Igel C, Vind I, Burisch TPP Johan. OP07 Artificial intelligence surpasses gastrointestinal experts in the classification of endoscopic severity among Ulcerative Colitis. Journal of Crohn’s and Colitis. 2021 05;15(Supplement):S007–S007. Vaze et al. [2022] Vaze S, Han K, Vedaldi A, Zisserman A. Open-set recognition: A good closed-set classifier is all you need? In: International Conference on Learning Representations (ICLR); 2022. . Gottlieb et al. [2021] Gottlieb K, Requa J, Karnes W, Chandra Gudivada R, Shen J, Rael E, et al. Central Reading of Ulcerative Colitis Clinical Trial Videos Using Neural Networks. Gastroenterology. 2021;160(3):710–719.e2. Woo et al. [2023] Woo S, Debnath S, Hu R, Chen X, Liu Z, Kweon IS, et al. ConvNeXt V2: Co-designing and Scaling ConvNets with Masked Autoencoders. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR); 2023. p. 6133–16142. Deng et al. [2009] Deng J, Dong W, Socher R, Li LJ, Li K, Fei-Fei L. Imagenet: A large-scale hierarchical image database. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR); 2009. p. 248–255. Guo et al. [2017] Guo C, Pleiss G, Sun Y, Weinberger KQ. On Calibration of Modern Neural Networks. In: Precup O, Teh YW, editors. Proceedings of the 34th International Conference on Machine Learning. vol. 70 of Proceedings of Machine Learning Research. PMLR; 2017. p. 1321–1330. Kishi et al. [2022] Kishi M, Hirai F, Takatsu N, et al. A review on the current status and definitions of activity indices in inflammatory bowel disease: how to use indices for precise evaluation. Journal of Gastroenterology. 2022;14:246–266. Vashist N, Samaan M, Mosli M, Parker C, MacDonald J, Nelson S, et al. Endoscopic scoring indices for evaluation of disease activity in ulcerative colitis. Cohrane Database of Systematic Reviews. 2018;1(1):CD011450. Ali [2022] Ali S. Where do we stand in AI for endoscopic image analysis? Deciphering gaps and future directions. npj Digital Medicine. 2022;5(1):184. Lo and Burisch [2021] Lo B, Burisch J. Artificial intelligence assisted assessment of endoscopic disease activity in inflammatory bowel disease. Artificial Intelligence in Gastrointestinal Endoscopy. 2021;2(4):95–102. Lo et al. [2022] Lo B, Liu Z, Bendtsen F, Igel C, Vind I, Burisch J. High accuracy in classifying endoscopic severity in ulcerative colitis using convolutional neural network. The American Journal of Gastroenterology. 2022;117(11):1648–1654. Becker et al. [2021] Becker BG, Arcadu F, Thalhammer A, Serna CG, Feehan O, Drawnel F, et al. Training and deploying a deep learning model for endoscopic severity grading in ulcerative colitis using multicenter clinical trial data. Therapeutic Advances in Gastrointestinal Endoscopy. 2021;14:2631774521990623. Lo et al. [2021] Lo B, Liu Z, Bendtsen F, Igel C, Vind I, Burisch TPP Johan. OP07 Artificial intelligence surpasses gastrointestinal experts in the classification of endoscopic severity among Ulcerative Colitis. Journal of Crohn’s and Colitis. 2021 05;15(Supplement):S007–S007. Vaze et al. [2022] Vaze S, Han K, Vedaldi A, Zisserman A. Open-set recognition: A good closed-set classifier is all you need? In: International Conference on Learning Representations (ICLR); 2022. . Gottlieb et al. [2021] Gottlieb K, Requa J, Karnes W, Chandra Gudivada R, Shen J, Rael E, et al. Central Reading of Ulcerative Colitis Clinical Trial Videos Using Neural Networks. Gastroenterology. 2021;160(3):710–719.e2. Woo et al. [2023] Woo S, Debnath S, Hu R, Chen X, Liu Z, Kweon IS, et al. ConvNeXt V2: Co-designing and Scaling ConvNets with Masked Autoencoders. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR); 2023. p. 6133–16142. Deng et al. [2009] Deng J, Dong W, Socher R, Li LJ, Li K, Fei-Fei L. Imagenet: A large-scale hierarchical image database. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR); 2009. p. 248–255. Guo et al. [2017] Guo C, Pleiss G, Sun Y, Weinberger KQ. On Calibration of Modern Neural Networks. In: Precup O, Teh YW, editors. Proceedings of the 34th International Conference on Machine Learning. vol. 70 of Proceedings of Machine Learning Research. PMLR; 2017. p. 1321–1330. Kishi et al. [2022] Kishi M, Hirai F, Takatsu N, et al. A review on the current status and definitions of activity indices in inflammatory bowel disease: how to use indices for precise evaluation. Journal of Gastroenterology. 2022;14:246–266. Ali S. Where do we stand in AI for endoscopic image analysis? Deciphering gaps and future directions. npj Digital Medicine. 2022;5(1):184. Lo and Burisch [2021] Lo B, Burisch J. Artificial intelligence assisted assessment of endoscopic disease activity in inflammatory bowel disease. Artificial Intelligence in Gastrointestinal Endoscopy. 2021;2(4):95–102. Lo et al. [2022] Lo B, Liu Z, Bendtsen F, Igel C, Vind I, Burisch J. High accuracy in classifying endoscopic severity in ulcerative colitis using convolutional neural network. The American Journal of Gastroenterology. 2022;117(11):1648–1654. Becker et al. [2021] Becker BG, Arcadu F, Thalhammer A, Serna CG, Feehan O, Drawnel F, et al. Training and deploying a deep learning model for endoscopic severity grading in ulcerative colitis using multicenter clinical trial data. Therapeutic Advances in Gastrointestinal Endoscopy. 2021;14:2631774521990623. Lo et al. [2021] Lo B, Liu Z, Bendtsen F, Igel C, Vind I, Burisch TPP Johan. OP07 Artificial intelligence surpasses gastrointestinal experts in the classification of endoscopic severity among Ulcerative Colitis. Journal of Crohn’s and Colitis. 2021 05;15(Supplement):S007–S007. Vaze et al. [2022] Vaze S, Han K, Vedaldi A, Zisserman A. Open-set recognition: A good closed-set classifier is all you need? In: International Conference on Learning Representations (ICLR); 2022. . Gottlieb et al. [2021] Gottlieb K, Requa J, Karnes W, Chandra Gudivada R, Shen J, Rael E, et al. Central Reading of Ulcerative Colitis Clinical Trial Videos Using Neural Networks. Gastroenterology. 2021;160(3):710–719.e2. Woo et al. [2023] Woo S, Debnath S, Hu R, Chen X, Liu Z, Kweon IS, et al. ConvNeXt V2: Co-designing and Scaling ConvNets with Masked Autoencoders. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR); 2023. p. 6133–16142. Deng et al. [2009] Deng J, Dong W, Socher R, Li LJ, Li K, Fei-Fei L. Imagenet: A large-scale hierarchical image database. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR); 2009. p. 248–255. Guo et al. [2017] Guo C, Pleiss G, Sun Y, Weinberger KQ. On Calibration of Modern Neural Networks. In: Precup O, Teh YW, editors. Proceedings of the 34th International Conference on Machine Learning. vol. 70 of Proceedings of Machine Learning Research. PMLR; 2017. p. 1321–1330. Kishi et al. [2022] Kishi M, Hirai F, Takatsu N, et al. A review on the current status and definitions of activity indices in inflammatory bowel disease: how to use indices for precise evaluation. Journal of Gastroenterology. 2022;14:246–266. Lo B, Burisch J. Artificial intelligence assisted assessment of endoscopic disease activity in inflammatory bowel disease. Artificial Intelligence in Gastrointestinal Endoscopy. 2021;2(4):95–102. Lo et al. [2022] Lo B, Liu Z, Bendtsen F, Igel C, Vind I, Burisch J. High accuracy in classifying endoscopic severity in ulcerative colitis using convolutional neural network. The American Journal of Gastroenterology. 2022;117(11):1648–1654. Becker et al. [2021] Becker BG, Arcadu F, Thalhammer A, Serna CG, Feehan O, Drawnel F, et al. Training and deploying a deep learning model for endoscopic severity grading in ulcerative colitis using multicenter clinical trial data. Therapeutic Advances in Gastrointestinal Endoscopy. 2021;14:2631774521990623. Lo et al. [2021] Lo B, Liu Z, Bendtsen F, Igel C, Vind I, Burisch TPP Johan. OP07 Artificial intelligence surpasses gastrointestinal experts in the classification of endoscopic severity among Ulcerative Colitis. Journal of Crohn’s and Colitis. 2021 05;15(Supplement):S007–S007. Vaze et al. [2022] Vaze S, Han K, Vedaldi A, Zisserman A. Open-set recognition: A good closed-set classifier is all you need? In: International Conference on Learning Representations (ICLR); 2022. . Gottlieb et al. [2021] Gottlieb K, Requa J, Karnes W, Chandra Gudivada R, Shen J, Rael E, et al. Central Reading of Ulcerative Colitis Clinical Trial Videos Using Neural Networks. Gastroenterology. 2021;160(3):710–719.e2. Woo et al. [2023] Woo S, Debnath S, Hu R, Chen X, Liu Z, Kweon IS, et al. ConvNeXt V2: Co-designing and Scaling ConvNets with Masked Autoencoders. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR); 2023. p. 6133–16142. Deng et al. [2009] Deng J, Dong W, Socher R, Li LJ, Li K, Fei-Fei L. Imagenet: A large-scale hierarchical image database. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR); 2009. p. 248–255. Guo et al. [2017] Guo C, Pleiss G, Sun Y, Weinberger KQ. On Calibration of Modern Neural Networks. In: Precup O, Teh YW, editors. Proceedings of the 34th International Conference on Machine Learning. vol. 70 of Proceedings of Machine Learning Research. PMLR; 2017. p. 1321–1330. Kishi et al. [2022] Kishi M, Hirai F, Takatsu N, et al. A review on the current status and definitions of activity indices in inflammatory bowel disease: how to use indices for precise evaluation. Journal of Gastroenterology. 2022;14:246–266. Lo B, Liu Z, Bendtsen F, Igel C, Vind I, Burisch J. High accuracy in classifying endoscopic severity in ulcerative colitis using convolutional neural network. The American Journal of Gastroenterology. 2022;117(11):1648–1654. Becker et al. [2021] Becker BG, Arcadu F, Thalhammer A, Serna CG, Feehan O, Drawnel F, et al. Training and deploying a deep learning model for endoscopic severity grading in ulcerative colitis using multicenter clinical trial data. Therapeutic Advances in Gastrointestinal Endoscopy. 2021;14:2631774521990623. Lo et al. [2021] Lo B, Liu Z, Bendtsen F, Igel C, Vind I, Burisch TPP Johan. OP07 Artificial intelligence surpasses gastrointestinal experts in the classification of endoscopic severity among Ulcerative Colitis. Journal of Crohn’s and Colitis. 2021 05;15(Supplement):S007–S007. Vaze et al. [2022] Vaze S, Han K, Vedaldi A, Zisserman A. Open-set recognition: A good closed-set classifier is all you need? In: International Conference on Learning Representations (ICLR); 2022. . Gottlieb et al. [2021] Gottlieb K, Requa J, Karnes W, Chandra Gudivada R, Shen J, Rael E, et al. Central Reading of Ulcerative Colitis Clinical Trial Videos Using Neural Networks. Gastroenterology. 2021;160(3):710–719.e2. Woo et al. [2023] Woo S, Debnath S, Hu R, Chen X, Liu Z, Kweon IS, et al. ConvNeXt V2: Co-designing and Scaling ConvNets with Masked Autoencoders. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR); 2023. p. 6133–16142. Deng et al. [2009] Deng J, Dong W, Socher R, Li LJ, Li K, Fei-Fei L. Imagenet: A large-scale hierarchical image database. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR); 2009. p. 248–255. Guo et al. [2017] Guo C, Pleiss G, Sun Y, Weinberger KQ. On Calibration of Modern Neural Networks. In: Precup O, Teh YW, editors. Proceedings of the 34th International Conference on Machine Learning. vol. 70 of Proceedings of Machine Learning Research. PMLR; 2017. p. 1321–1330. Kishi et al. [2022] Kishi M, Hirai F, Takatsu N, et al. A review on the current status and definitions of activity indices in inflammatory bowel disease: how to use indices for precise evaluation. Journal of Gastroenterology. 2022;14:246–266. Becker BG, Arcadu F, Thalhammer A, Serna CG, Feehan O, Drawnel F, et al. Training and deploying a deep learning model for endoscopic severity grading in ulcerative colitis using multicenter clinical trial data. Therapeutic Advances in Gastrointestinal Endoscopy. 2021;14:2631774521990623. Lo et al. [2021] Lo B, Liu Z, Bendtsen F, Igel C, Vind I, Burisch TPP Johan. OP07 Artificial intelligence surpasses gastrointestinal experts in the classification of endoscopic severity among Ulcerative Colitis. Journal of Crohn’s and Colitis. 2021 05;15(Supplement):S007–S007. Vaze et al. [2022] Vaze S, Han K, Vedaldi A, Zisserman A. Open-set recognition: A good closed-set classifier is all you need? In: International Conference on Learning Representations (ICLR); 2022. . Gottlieb et al. [2021] Gottlieb K, Requa J, Karnes W, Chandra Gudivada R, Shen J, Rael E, et al. Central Reading of Ulcerative Colitis Clinical Trial Videos Using Neural Networks. Gastroenterology. 2021;160(3):710–719.e2. Woo et al. [2023] Woo S, Debnath S, Hu R, Chen X, Liu Z, Kweon IS, et al. ConvNeXt V2: Co-designing and Scaling ConvNets with Masked Autoencoders. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR); 2023. p. 6133–16142. Deng et al. [2009] Deng J, Dong W, Socher R, Li LJ, Li K, Fei-Fei L. Imagenet: A large-scale hierarchical image database. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR); 2009. p. 248–255. Guo et al. [2017] Guo C, Pleiss G, Sun Y, Weinberger KQ. On Calibration of Modern Neural Networks. In: Precup O, Teh YW, editors. Proceedings of the 34th International Conference on Machine Learning. vol. 70 of Proceedings of Machine Learning Research. PMLR; 2017. p. 1321–1330. Kishi et al. [2022] Kishi M, Hirai F, Takatsu N, et al. A review on the current status and definitions of activity indices in inflammatory bowel disease: how to use indices for precise evaluation. Journal of Gastroenterology. 2022;14:246–266. Lo B, Liu Z, Bendtsen F, Igel C, Vind I, Burisch TPP Johan. OP07 Artificial intelligence surpasses gastrointestinal experts in the classification of endoscopic severity among Ulcerative Colitis. Journal of Crohn’s and Colitis. 2021 05;15(Supplement):S007–S007. Vaze et al. [2022] Vaze S, Han K, Vedaldi A, Zisserman A. Open-set recognition: A good closed-set classifier is all you need? In: International Conference on Learning Representations (ICLR); 2022. . Gottlieb et al. [2021] Gottlieb K, Requa J, Karnes W, Chandra Gudivada R, Shen J, Rael E, et al. Central Reading of Ulcerative Colitis Clinical Trial Videos Using Neural Networks. Gastroenterology. 2021;160(3):710–719.e2. Woo et al. [2023] Woo S, Debnath S, Hu R, Chen X, Liu Z, Kweon IS, et al. ConvNeXt V2: Co-designing and Scaling ConvNets with Masked Autoencoders. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR); 2023. p. 6133–16142. Deng et al. [2009] Deng J, Dong W, Socher R, Li LJ, Li K, Fei-Fei L. Imagenet: A large-scale hierarchical image database. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR); 2009. p. 248–255. Guo et al. [2017] Guo C, Pleiss G, Sun Y, Weinberger KQ. On Calibration of Modern Neural Networks. In: Precup O, Teh YW, editors. Proceedings of the 34th International Conference on Machine Learning. vol. 70 of Proceedings of Machine Learning Research. PMLR; 2017. p. 1321–1330. Kishi et al. [2022] Kishi M, Hirai F, Takatsu N, et al. A review on the current status and definitions of activity indices in inflammatory bowel disease: how to use indices for precise evaluation. Journal of Gastroenterology. 2022;14:246–266. Vaze S, Han K, Vedaldi A, Zisserman A. Open-set recognition: A good closed-set classifier is all you need? In: International Conference on Learning Representations (ICLR); 2022. . Gottlieb et al. [2021] Gottlieb K, Requa J, Karnes W, Chandra Gudivada R, Shen J, Rael E, et al. Central Reading of Ulcerative Colitis Clinical Trial Videos Using Neural Networks. Gastroenterology. 2021;160(3):710–719.e2. Woo et al. [2023] Woo S, Debnath S, Hu R, Chen X, Liu Z, Kweon IS, et al. ConvNeXt V2: Co-designing and Scaling ConvNets with Masked Autoencoders. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR); 2023. p. 6133–16142. Deng et al. [2009] Deng J, Dong W, Socher R, Li LJ, Li K, Fei-Fei L. Imagenet: A large-scale hierarchical image database. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR); 2009. p. 248–255. Guo et al. [2017] Guo C, Pleiss G, Sun Y, Weinberger KQ. On Calibration of Modern Neural Networks. In: Precup O, Teh YW, editors. Proceedings of the 34th International Conference on Machine Learning. vol. 70 of Proceedings of Machine Learning Research. PMLR; 2017. p. 1321–1330. Kishi et al. [2022] Kishi M, Hirai F, Takatsu N, et al. A review on the current status and definitions of activity indices in inflammatory bowel disease: how to use indices for precise evaluation. Journal of Gastroenterology. 2022;14:246–266. Gottlieb K, Requa J, Karnes W, Chandra Gudivada R, Shen J, Rael E, et al. Central Reading of Ulcerative Colitis Clinical Trial Videos Using Neural Networks. Gastroenterology. 2021;160(3):710–719.e2. Woo et al. [2023] Woo S, Debnath S, Hu R, Chen X, Liu Z, Kweon IS, et al. ConvNeXt V2: Co-designing and Scaling ConvNets with Masked Autoencoders. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR); 2023. p. 6133–16142. Deng et al. [2009] Deng J, Dong W, Socher R, Li LJ, Li K, Fei-Fei L. Imagenet: A large-scale hierarchical image database. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR); 2009. p. 248–255. Guo et al. [2017] Guo C, Pleiss G, Sun Y, Weinberger KQ. On Calibration of Modern Neural Networks. In: Precup O, Teh YW, editors. Proceedings of the 34th International Conference on Machine Learning. vol. 70 of Proceedings of Machine Learning Research. PMLR; 2017. p. 1321–1330. Kishi et al. [2022] Kishi M, Hirai F, Takatsu N, et al. A review on the current status and definitions of activity indices in inflammatory bowel disease: how to use indices for precise evaluation. Journal of Gastroenterology. 2022;14:246–266. Woo S, Debnath S, Hu R, Chen X, Liu Z, Kweon IS, et al. ConvNeXt V2: Co-designing and Scaling ConvNets with Masked Autoencoders. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR); 2023. p. 6133–16142. Deng et al. [2009] Deng J, Dong W, Socher R, Li LJ, Li K, Fei-Fei L. Imagenet: A large-scale hierarchical image database. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR); 2009. p. 248–255. Guo et al. [2017] Guo C, Pleiss G, Sun Y, Weinberger KQ. On Calibration of Modern Neural Networks. In: Precup O, Teh YW, editors. Proceedings of the 34th International Conference on Machine Learning. vol. 70 of Proceedings of Machine Learning Research. PMLR; 2017. p. 1321–1330. Kishi et al. [2022] Kishi M, Hirai F, Takatsu N, et al. A review on the current status and definitions of activity indices in inflammatory bowel disease: how to use indices for precise evaluation. Journal of Gastroenterology. 2022;14:246–266. Deng J, Dong W, Socher R, Li LJ, Li K, Fei-Fei L. Imagenet: A large-scale hierarchical image database. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR); 2009. p. 248–255. Guo et al. [2017] Guo C, Pleiss G, Sun Y, Weinberger KQ. On Calibration of Modern Neural Networks. In: Precup O, Teh YW, editors. Proceedings of the 34th International Conference on Machine Learning. vol. 70 of Proceedings of Machine Learning Research. PMLR; 2017. p. 1321–1330. Kishi et al. [2022] Kishi M, Hirai F, Takatsu N, et al. A review on the current status and definitions of activity indices in inflammatory bowel disease: how to use indices for precise evaluation. Journal of Gastroenterology. 2022;14:246–266. Guo C, Pleiss G, Sun Y, Weinberger KQ. On Calibration of Modern Neural Networks. In: Precup O, Teh YW, editors. Proceedings of the 34th International Conference on Machine Learning. vol. 70 of Proceedings of Machine Learning Research. PMLR; 2017. p. 1321–1330. Kishi et al. [2022] Kishi M, Hirai F, Takatsu N, et al. A review on the current status and definitions of activity indices in inflammatory bowel disease: how to use indices for precise evaluation. Journal of Gastroenterology. 2022;14:246–266. Kishi M, Hirai F, Takatsu N, et al. A review on the current status and definitions of activity indices in inflammatory bowel disease: how to use indices for precise evaluation. Journal of Gastroenterology. 2022;14:246–266.
- Coated Oral 5-Aminosalicylic Acid Therapy for Mildly to Moderately Active Ulcerative Colitis. New England Journal of Medicine. 1987;317(26):1625–1629. PMID: 3317057. Samaan et al. [2014] Samaan MA, Mosli MH, Sandborn WJ, Feagan BG, D’Haens GR, Dubcenco E, et al. A Systematic Review of the Measurement of Endoscopic Healing in Ulcerative Colitis Clinical Trials: Recommendations and Implications for Future Research. Inflammatory Bowel Diseases. 2014 05;20(8):1465–1471. Vashist et al. [2018] Vashist N, Samaan M, Mosli M, Parker C, MacDonald J, Nelson S, et al. Endoscopic scoring indices for evaluation of disease activity in ulcerative colitis. Cohrane Database of Systematic Reviews. 2018;1(1):CD011450. Ali [2022] Ali S. Where do we stand in AI for endoscopic image analysis? Deciphering gaps and future directions. npj Digital Medicine. 2022;5(1):184. Lo and Burisch [2021] Lo B, Burisch J. Artificial intelligence assisted assessment of endoscopic disease activity in inflammatory bowel disease. Artificial Intelligence in Gastrointestinal Endoscopy. 2021;2(4):95–102. Lo et al. [2022] Lo B, Liu Z, Bendtsen F, Igel C, Vind I, Burisch J. High accuracy in classifying endoscopic severity in ulcerative colitis using convolutional neural network. The American Journal of Gastroenterology. 2022;117(11):1648–1654. Becker et al. [2021] Becker BG, Arcadu F, Thalhammer A, Serna CG, Feehan O, Drawnel F, et al. Training and deploying a deep learning model for endoscopic severity grading in ulcerative colitis using multicenter clinical trial data. Therapeutic Advances in Gastrointestinal Endoscopy. 2021;14:2631774521990623. Lo et al. [2021] Lo B, Liu Z, Bendtsen F, Igel C, Vind I, Burisch TPP Johan. OP07 Artificial intelligence surpasses gastrointestinal experts in the classification of endoscopic severity among Ulcerative Colitis. Journal of Crohn’s and Colitis. 2021 05;15(Supplement):S007–S007. Vaze et al. [2022] Vaze S, Han K, Vedaldi A, Zisserman A. Open-set recognition: A good closed-set classifier is all you need? In: International Conference on Learning Representations (ICLR); 2022. . Gottlieb et al. [2021] Gottlieb K, Requa J, Karnes W, Chandra Gudivada R, Shen J, Rael E, et al. Central Reading of Ulcerative Colitis Clinical Trial Videos Using Neural Networks. Gastroenterology. 2021;160(3):710–719.e2. Woo et al. [2023] Woo S, Debnath S, Hu R, Chen X, Liu Z, Kweon IS, et al. ConvNeXt V2: Co-designing and Scaling ConvNets with Masked Autoencoders. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR); 2023. p. 6133–16142. Deng et al. [2009] Deng J, Dong W, Socher R, Li LJ, Li K, Fei-Fei L. Imagenet: A large-scale hierarchical image database. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR); 2009. p. 248–255. Guo et al. [2017] Guo C, Pleiss G, Sun Y, Weinberger KQ. On Calibration of Modern Neural Networks. In: Precup O, Teh YW, editors. Proceedings of the 34th International Conference on Machine Learning. vol. 70 of Proceedings of Machine Learning Research. PMLR; 2017. p. 1321–1330. Kishi et al. [2022] Kishi M, Hirai F, Takatsu N, et al. A review on the current status and definitions of activity indices in inflammatory bowel disease: how to use indices for precise evaluation. Journal of Gastroenterology. 2022;14:246–266. Samaan MA, Mosli MH, Sandborn WJ, Feagan BG, D’Haens GR, Dubcenco E, et al. A Systematic Review of the Measurement of Endoscopic Healing in Ulcerative Colitis Clinical Trials: Recommendations and Implications for Future Research. Inflammatory Bowel Diseases. 2014 05;20(8):1465–1471. Vashist et al. [2018] Vashist N, Samaan M, Mosli M, Parker C, MacDonald J, Nelson S, et al. Endoscopic scoring indices for evaluation of disease activity in ulcerative colitis. Cohrane Database of Systematic Reviews. 2018;1(1):CD011450. Ali [2022] Ali S. Where do we stand in AI for endoscopic image analysis? Deciphering gaps and future directions. npj Digital Medicine. 2022;5(1):184. Lo and Burisch [2021] Lo B, Burisch J. Artificial intelligence assisted assessment of endoscopic disease activity in inflammatory bowel disease. Artificial Intelligence in Gastrointestinal Endoscopy. 2021;2(4):95–102. Lo et al. [2022] Lo B, Liu Z, Bendtsen F, Igel C, Vind I, Burisch J. High accuracy in classifying endoscopic severity in ulcerative colitis using convolutional neural network. The American Journal of Gastroenterology. 2022;117(11):1648–1654. Becker et al. [2021] Becker BG, Arcadu F, Thalhammer A, Serna CG, Feehan O, Drawnel F, et al. Training and deploying a deep learning model for endoscopic severity grading in ulcerative colitis using multicenter clinical trial data. Therapeutic Advances in Gastrointestinal Endoscopy. 2021;14:2631774521990623. Lo et al. [2021] Lo B, Liu Z, Bendtsen F, Igel C, Vind I, Burisch TPP Johan. OP07 Artificial intelligence surpasses gastrointestinal experts in the classification of endoscopic severity among Ulcerative Colitis. Journal of Crohn’s and Colitis. 2021 05;15(Supplement):S007–S007. Vaze et al. [2022] Vaze S, Han K, Vedaldi A, Zisserman A. Open-set recognition: A good closed-set classifier is all you need? In: International Conference on Learning Representations (ICLR); 2022. . Gottlieb et al. [2021] Gottlieb K, Requa J, Karnes W, Chandra Gudivada R, Shen J, Rael E, et al. Central Reading of Ulcerative Colitis Clinical Trial Videos Using Neural Networks. Gastroenterology. 2021;160(3):710–719.e2. Woo et al. [2023] Woo S, Debnath S, Hu R, Chen X, Liu Z, Kweon IS, et al. ConvNeXt V2: Co-designing and Scaling ConvNets with Masked Autoencoders. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR); 2023. p. 6133–16142. Deng et al. [2009] Deng J, Dong W, Socher R, Li LJ, Li K, Fei-Fei L. Imagenet: A large-scale hierarchical image database. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR); 2009. p. 248–255. Guo et al. [2017] Guo C, Pleiss G, Sun Y, Weinberger KQ. On Calibration of Modern Neural Networks. In: Precup O, Teh YW, editors. Proceedings of the 34th International Conference on Machine Learning. vol. 70 of Proceedings of Machine Learning Research. PMLR; 2017. p. 1321–1330. Kishi et al. [2022] Kishi M, Hirai F, Takatsu N, et al. A review on the current status and definitions of activity indices in inflammatory bowel disease: how to use indices for precise evaluation. Journal of Gastroenterology. 2022;14:246–266. Vashist N, Samaan M, Mosli M, Parker C, MacDonald J, Nelson S, et al. Endoscopic scoring indices for evaluation of disease activity in ulcerative colitis. Cohrane Database of Systematic Reviews. 2018;1(1):CD011450. Ali [2022] Ali S. Where do we stand in AI for endoscopic image analysis? Deciphering gaps and future directions. npj Digital Medicine. 2022;5(1):184. Lo and Burisch [2021] Lo B, Burisch J. Artificial intelligence assisted assessment of endoscopic disease activity in inflammatory bowel disease. Artificial Intelligence in Gastrointestinal Endoscopy. 2021;2(4):95–102. Lo et al. [2022] Lo B, Liu Z, Bendtsen F, Igel C, Vind I, Burisch J. High accuracy in classifying endoscopic severity in ulcerative colitis using convolutional neural network. The American Journal of Gastroenterology. 2022;117(11):1648–1654. Becker et al. [2021] Becker BG, Arcadu F, Thalhammer A, Serna CG, Feehan O, Drawnel F, et al. Training and deploying a deep learning model for endoscopic severity grading in ulcerative colitis using multicenter clinical trial data. Therapeutic Advances in Gastrointestinal Endoscopy. 2021;14:2631774521990623. Lo et al. [2021] Lo B, Liu Z, Bendtsen F, Igel C, Vind I, Burisch TPP Johan. OP07 Artificial intelligence surpasses gastrointestinal experts in the classification of endoscopic severity among Ulcerative Colitis. Journal of Crohn’s and Colitis. 2021 05;15(Supplement):S007–S007. Vaze et al. [2022] Vaze S, Han K, Vedaldi A, Zisserman A. Open-set recognition: A good closed-set classifier is all you need? In: International Conference on Learning Representations (ICLR); 2022. . Gottlieb et al. [2021] Gottlieb K, Requa J, Karnes W, Chandra Gudivada R, Shen J, Rael E, et al. Central Reading of Ulcerative Colitis Clinical Trial Videos Using Neural Networks. Gastroenterology. 2021;160(3):710–719.e2. Woo et al. [2023] Woo S, Debnath S, Hu R, Chen X, Liu Z, Kweon IS, et al. ConvNeXt V2: Co-designing and Scaling ConvNets with Masked Autoencoders. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR); 2023. p. 6133–16142. Deng et al. [2009] Deng J, Dong W, Socher R, Li LJ, Li K, Fei-Fei L. Imagenet: A large-scale hierarchical image database. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR); 2009. p. 248–255. Guo et al. [2017] Guo C, Pleiss G, Sun Y, Weinberger KQ. On Calibration of Modern Neural Networks. In: Precup O, Teh YW, editors. Proceedings of the 34th International Conference on Machine Learning. vol. 70 of Proceedings of Machine Learning Research. PMLR; 2017. p. 1321–1330. Kishi et al. [2022] Kishi M, Hirai F, Takatsu N, et al. A review on the current status and definitions of activity indices in inflammatory bowel disease: how to use indices for precise evaluation. Journal of Gastroenterology. 2022;14:246–266. Ali S. Where do we stand in AI for endoscopic image analysis? Deciphering gaps and future directions. npj Digital Medicine. 2022;5(1):184. Lo and Burisch [2021] Lo B, Burisch J. Artificial intelligence assisted assessment of endoscopic disease activity in inflammatory bowel disease. Artificial Intelligence in Gastrointestinal Endoscopy. 2021;2(4):95–102. Lo et al. [2022] Lo B, Liu Z, Bendtsen F, Igel C, Vind I, Burisch J. High accuracy in classifying endoscopic severity in ulcerative colitis using convolutional neural network. The American Journal of Gastroenterology. 2022;117(11):1648–1654. Becker et al. [2021] Becker BG, Arcadu F, Thalhammer A, Serna CG, Feehan O, Drawnel F, et al. Training and deploying a deep learning model for endoscopic severity grading in ulcerative colitis using multicenter clinical trial data. Therapeutic Advances in Gastrointestinal Endoscopy. 2021;14:2631774521990623. Lo et al. [2021] Lo B, Liu Z, Bendtsen F, Igel C, Vind I, Burisch TPP Johan. OP07 Artificial intelligence surpasses gastrointestinal experts in the classification of endoscopic severity among Ulcerative Colitis. Journal of Crohn’s and Colitis. 2021 05;15(Supplement):S007–S007. Vaze et al. [2022] Vaze S, Han K, Vedaldi A, Zisserman A. Open-set recognition: A good closed-set classifier is all you need? In: International Conference on Learning Representations (ICLR); 2022. . Gottlieb et al. [2021] Gottlieb K, Requa J, Karnes W, Chandra Gudivada R, Shen J, Rael E, et al. Central Reading of Ulcerative Colitis Clinical Trial Videos Using Neural Networks. Gastroenterology. 2021;160(3):710–719.e2. Woo et al. [2023] Woo S, Debnath S, Hu R, Chen X, Liu Z, Kweon IS, et al. ConvNeXt V2: Co-designing and Scaling ConvNets with Masked Autoencoders. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR); 2023. p. 6133–16142. Deng et al. [2009] Deng J, Dong W, Socher R, Li LJ, Li K, Fei-Fei L. Imagenet: A large-scale hierarchical image database. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR); 2009. p. 248–255. Guo et al. [2017] Guo C, Pleiss G, Sun Y, Weinberger KQ. On Calibration of Modern Neural Networks. In: Precup O, Teh YW, editors. Proceedings of the 34th International Conference on Machine Learning. vol. 70 of Proceedings of Machine Learning Research. PMLR; 2017. p. 1321–1330. Kishi et al. [2022] Kishi M, Hirai F, Takatsu N, et al. A review on the current status and definitions of activity indices in inflammatory bowel disease: how to use indices for precise evaluation. Journal of Gastroenterology. 2022;14:246–266. Lo B, Burisch J. Artificial intelligence assisted assessment of endoscopic disease activity in inflammatory bowel disease. Artificial Intelligence in Gastrointestinal Endoscopy. 2021;2(4):95–102. Lo et al. [2022] Lo B, Liu Z, Bendtsen F, Igel C, Vind I, Burisch J. High accuracy in classifying endoscopic severity in ulcerative colitis using convolutional neural network. The American Journal of Gastroenterology. 2022;117(11):1648–1654. Becker et al. [2021] Becker BG, Arcadu F, Thalhammer A, Serna CG, Feehan O, Drawnel F, et al. Training and deploying a deep learning model for endoscopic severity grading in ulcerative colitis using multicenter clinical trial data. Therapeutic Advances in Gastrointestinal Endoscopy. 2021;14:2631774521990623. Lo et al. [2021] Lo B, Liu Z, Bendtsen F, Igel C, Vind I, Burisch TPP Johan. OP07 Artificial intelligence surpasses gastrointestinal experts in the classification of endoscopic severity among Ulcerative Colitis. Journal of Crohn’s and Colitis. 2021 05;15(Supplement):S007–S007. Vaze et al. [2022] Vaze S, Han K, Vedaldi A, Zisserman A. Open-set recognition: A good closed-set classifier is all you need? In: International Conference on Learning Representations (ICLR); 2022. . Gottlieb et al. [2021] Gottlieb K, Requa J, Karnes W, Chandra Gudivada R, Shen J, Rael E, et al. Central Reading of Ulcerative Colitis Clinical Trial Videos Using Neural Networks. Gastroenterology. 2021;160(3):710–719.e2. Woo et al. [2023] Woo S, Debnath S, Hu R, Chen X, Liu Z, Kweon IS, et al. ConvNeXt V2: Co-designing and Scaling ConvNets with Masked Autoencoders. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR); 2023. p. 6133–16142. Deng et al. [2009] Deng J, Dong W, Socher R, Li LJ, Li K, Fei-Fei L. Imagenet: A large-scale hierarchical image database. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR); 2009. p. 248–255. Guo et al. [2017] Guo C, Pleiss G, Sun Y, Weinberger KQ. On Calibration of Modern Neural Networks. In: Precup O, Teh YW, editors. Proceedings of the 34th International Conference on Machine Learning. vol. 70 of Proceedings of Machine Learning Research. PMLR; 2017. p. 1321–1330. Kishi et al. [2022] Kishi M, Hirai F, Takatsu N, et al. A review on the current status and definitions of activity indices in inflammatory bowel disease: how to use indices for precise evaluation. Journal of Gastroenterology. 2022;14:246–266. Lo B, Liu Z, Bendtsen F, Igel C, Vind I, Burisch J. High accuracy in classifying endoscopic severity in ulcerative colitis using convolutional neural network. The American Journal of Gastroenterology. 2022;117(11):1648–1654. Becker et al. [2021] Becker BG, Arcadu F, Thalhammer A, Serna CG, Feehan O, Drawnel F, et al. Training and deploying a deep learning model for endoscopic severity grading in ulcerative colitis using multicenter clinical trial data. Therapeutic Advances in Gastrointestinal Endoscopy. 2021;14:2631774521990623. Lo et al. [2021] Lo B, Liu Z, Bendtsen F, Igel C, Vind I, Burisch TPP Johan. OP07 Artificial intelligence surpasses gastrointestinal experts in the classification of endoscopic severity among Ulcerative Colitis. Journal of Crohn’s and Colitis. 2021 05;15(Supplement):S007–S007. Vaze et al. [2022] Vaze S, Han K, Vedaldi A, Zisserman A. Open-set recognition: A good closed-set classifier is all you need? In: International Conference on Learning Representations (ICLR); 2022. . Gottlieb et al. [2021] Gottlieb K, Requa J, Karnes W, Chandra Gudivada R, Shen J, Rael E, et al. Central Reading of Ulcerative Colitis Clinical Trial Videos Using Neural Networks. Gastroenterology. 2021;160(3):710–719.e2. Woo et al. [2023] Woo S, Debnath S, Hu R, Chen X, Liu Z, Kweon IS, et al. ConvNeXt V2: Co-designing and Scaling ConvNets with Masked Autoencoders. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR); 2023. p. 6133–16142. Deng et al. [2009] Deng J, Dong W, Socher R, Li LJ, Li K, Fei-Fei L. Imagenet: A large-scale hierarchical image database. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR); 2009. p. 248–255. Guo et al. [2017] Guo C, Pleiss G, Sun Y, Weinberger KQ. On Calibration of Modern Neural Networks. In: Precup O, Teh YW, editors. Proceedings of the 34th International Conference on Machine Learning. vol. 70 of Proceedings of Machine Learning Research. PMLR; 2017. p. 1321–1330. Kishi et al. [2022] Kishi M, Hirai F, Takatsu N, et al. A review on the current status and definitions of activity indices in inflammatory bowel disease: how to use indices for precise evaluation. Journal of Gastroenterology. 2022;14:246–266. Becker BG, Arcadu F, Thalhammer A, Serna CG, Feehan O, Drawnel F, et al. Training and deploying a deep learning model for endoscopic severity grading in ulcerative colitis using multicenter clinical trial data. Therapeutic Advances in Gastrointestinal Endoscopy. 2021;14:2631774521990623. Lo et al. [2021] Lo B, Liu Z, Bendtsen F, Igel C, Vind I, Burisch TPP Johan. OP07 Artificial intelligence surpasses gastrointestinal experts in the classification of endoscopic severity among Ulcerative Colitis. Journal of Crohn’s and Colitis. 2021 05;15(Supplement):S007–S007. Vaze et al. [2022] Vaze S, Han K, Vedaldi A, Zisserman A. Open-set recognition: A good closed-set classifier is all you need? In: International Conference on Learning Representations (ICLR); 2022. . Gottlieb et al. [2021] Gottlieb K, Requa J, Karnes W, Chandra Gudivada R, Shen J, Rael E, et al. Central Reading of Ulcerative Colitis Clinical Trial Videos Using Neural Networks. Gastroenterology. 2021;160(3):710–719.e2. Woo et al. [2023] Woo S, Debnath S, Hu R, Chen X, Liu Z, Kweon IS, et al. ConvNeXt V2: Co-designing and Scaling ConvNets with Masked Autoencoders. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR); 2023. p. 6133–16142. Deng et al. [2009] Deng J, Dong W, Socher R, Li LJ, Li K, Fei-Fei L. Imagenet: A large-scale hierarchical image database. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR); 2009. p. 248–255. Guo et al. [2017] Guo C, Pleiss G, Sun Y, Weinberger KQ. On Calibration of Modern Neural Networks. In: Precup O, Teh YW, editors. Proceedings of the 34th International Conference on Machine Learning. vol. 70 of Proceedings of Machine Learning Research. PMLR; 2017. p. 1321–1330. Kishi et al. [2022] Kishi M, Hirai F, Takatsu N, et al. A review on the current status and definitions of activity indices in inflammatory bowel disease: how to use indices for precise evaluation. Journal of Gastroenterology. 2022;14:246–266. Lo B, Liu Z, Bendtsen F, Igel C, Vind I, Burisch TPP Johan. OP07 Artificial intelligence surpasses gastrointestinal experts in the classification of endoscopic severity among Ulcerative Colitis. Journal of Crohn’s and Colitis. 2021 05;15(Supplement):S007–S007. Vaze et al. [2022] Vaze S, Han K, Vedaldi A, Zisserman A. Open-set recognition: A good closed-set classifier is all you need? In: International Conference on Learning Representations (ICLR); 2022. . Gottlieb et al. [2021] Gottlieb K, Requa J, Karnes W, Chandra Gudivada R, Shen J, Rael E, et al. Central Reading of Ulcerative Colitis Clinical Trial Videos Using Neural Networks. Gastroenterology. 2021;160(3):710–719.e2. Woo et al. [2023] Woo S, Debnath S, Hu R, Chen X, Liu Z, Kweon IS, et al. ConvNeXt V2: Co-designing and Scaling ConvNets with Masked Autoencoders. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR); 2023. p. 6133–16142. Deng et al. [2009] Deng J, Dong W, Socher R, Li LJ, Li K, Fei-Fei L. Imagenet: A large-scale hierarchical image database. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR); 2009. p. 248–255. Guo et al. [2017] Guo C, Pleiss G, Sun Y, Weinberger KQ. On Calibration of Modern Neural Networks. In: Precup O, Teh YW, editors. Proceedings of the 34th International Conference on Machine Learning. vol. 70 of Proceedings of Machine Learning Research. PMLR; 2017. p. 1321–1330. Kishi et al. [2022] Kishi M, Hirai F, Takatsu N, et al. A review on the current status and definitions of activity indices in inflammatory bowel disease: how to use indices for precise evaluation. Journal of Gastroenterology. 2022;14:246–266. Vaze S, Han K, Vedaldi A, Zisserman A. Open-set recognition: A good closed-set classifier is all you need? In: International Conference on Learning Representations (ICLR); 2022. . Gottlieb et al. [2021] Gottlieb K, Requa J, Karnes W, Chandra Gudivada R, Shen J, Rael E, et al. Central Reading of Ulcerative Colitis Clinical Trial Videos Using Neural Networks. Gastroenterology. 2021;160(3):710–719.e2. Woo et al. [2023] Woo S, Debnath S, Hu R, Chen X, Liu Z, Kweon IS, et al. ConvNeXt V2: Co-designing and Scaling ConvNets with Masked Autoencoders. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR); 2023. p. 6133–16142. Deng et al. [2009] Deng J, Dong W, Socher R, Li LJ, Li K, Fei-Fei L. Imagenet: A large-scale hierarchical image database. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR); 2009. p. 248–255. Guo et al. [2017] Guo C, Pleiss G, Sun Y, Weinberger KQ. On Calibration of Modern Neural Networks. In: Precup O, Teh YW, editors. Proceedings of the 34th International Conference on Machine Learning. vol. 70 of Proceedings of Machine Learning Research. PMLR; 2017. p. 1321–1330. Kishi et al. [2022] Kishi M, Hirai F, Takatsu N, et al. A review on the current status and definitions of activity indices in inflammatory bowel disease: how to use indices for precise evaluation. Journal of Gastroenterology. 2022;14:246–266. Gottlieb K, Requa J, Karnes W, Chandra Gudivada R, Shen J, Rael E, et al. Central Reading of Ulcerative Colitis Clinical Trial Videos Using Neural Networks. Gastroenterology. 2021;160(3):710–719.e2. Woo et al. [2023] Woo S, Debnath S, Hu R, Chen X, Liu Z, Kweon IS, et al. ConvNeXt V2: Co-designing and Scaling ConvNets with Masked Autoencoders. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR); 2023. p. 6133–16142. Deng et al. [2009] Deng J, Dong W, Socher R, Li LJ, Li K, Fei-Fei L. Imagenet: A large-scale hierarchical image database. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR); 2009. p. 248–255. Guo et al. [2017] Guo C, Pleiss G, Sun Y, Weinberger KQ. On Calibration of Modern Neural Networks. In: Precup O, Teh YW, editors. Proceedings of the 34th International Conference on Machine Learning. vol. 70 of Proceedings of Machine Learning Research. PMLR; 2017. p. 1321–1330. Kishi et al. [2022] Kishi M, Hirai F, Takatsu N, et al. A review on the current status and definitions of activity indices in inflammatory bowel disease: how to use indices for precise evaluation. Journal of Gastroenterology. 2022;14:246–266. Woo S, Debnath S, Hu R, Chen X, Liu Z, Kweon IS, et al. ConvNeXt V2: Co-designing and Scaling ConvNets with Masked Autoencoders. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR); 2023. p. 6133–16142. Deng et al. [2009] Deng J, Dong W, Socher R, Li LJ, Li K, Fei-Fei L. Imagenet: A large-scale hierarchical image database. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR); 2009. p. 248–255. Guo et al. [2017] Guo C, Pleiss G, Sun Y, Weinberger KQ. On Calibration of Modern Neural Networks. In: Precup O, Teh YW, editors. Proceedings of the 34th International Conference on Machine Learning. vol. 70 of Proceedings of Machine Learning Research. PMLR; 2017. p. 1321–1330. Kishi et al. [2022] Kishi M, Hirai F, Takatsu N, et al. A review on the current status and definitions of activity indices in inflammatory bowel disease: how to use indices for precise evaluation. Journal of Gastroenterology. 2022;14:246–266. Deng J, Dong W, Socher R, Li LJ, Li K, Fei-Fei L. Imagenet: A large-scale hierarchical image database. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR); 2009. p. 248–255. Guo et al. [2017] Guo C, Pleiss G, Sun Y, Weinberger KQ. On Calibration of Modern Neural Networks. In: Precup O, Teh YW, editors. Proceedings of the 34th International Conference on Machine Learning. vol. 70 of Proceedings of Machine Learning Research. PMLR; 2017. p. 1321–1330. Kishi et al. [2022] Kishi M, Hirai F, Takatsu N, et al. A review on the current status and definitions of activity indices in inflammatory bowel disease: how to use indices for precise evaluation. Journal of Gastroenterology. 2022;14:246–266. Guo C, Pleiss G, Sun Y, Weinberger KQ. On Calibration of Modern Neural Networks. In: Precup O, Teh YW, editors. Proceedings of the 34th International Conference on Machine Learning. vol. 70 of Proceedings of Machine Learning Research. PMLR; 2017. p. 1321–1330. Kishi et al. [2022] Kishi M, Hirai F, Takatsu N, et al. A review on the current status and definitions of activity indices in inflammatory bowel disease: how to use indices for precise evaluation. Journal of Gastroenterology. 2022;14:246–266. Kishi M, Hirai F, Takatsu N, et al. A review on the current status and definitions of activity indices in inflammatory bowel disease: how to use indices for precise evaluation. Journal of Gastroenterology. 2022;14:246–266.
- A Systematic Review of the Measurement of Endoscopic Healing in Ulcerative Colitis Clinical Trials: Recommendations and Implications for Future Research. Inflammatory Bowel Diseases. 2014 05;20(8):1465–1471. Vashist et al. [2018] Vashist N, Samaan M, Mosli M, Parker C, MacDonald J, Nelson S, et al. Endoscopic scoring indices for evaluation of disease activity in ulcerative colitis. Cohrane Database of Systematic Reviews. 2018;1(1):CD011450. Ali [2022] Ali S. Where do we stand in AI for endoscopic image analysis? Deciphering gaps and future directions. npj Digital Medicine. 2022;5(1):184. Lo and Burisch [2021] Lo B, Burisch J. Artificial intelligence assisted assessment of endoscopic disease activity in inflammatory bowel disease. Artificial Intelligence in Gastrointestinal Endoscopy. 2021;2(4):95–102. Lo et al. [2022] Lo B, Liu Z, Bendtsen F, Igel C, Vind I, Burisch J. High accuracy in classifying endoscopic severity in ulcerative colitis using convolutional neural network. The American Journal of Gastroenterology. 2022;117(11):1648–1654. Becker et al. [2021] Becker BG, Arcadu F, Thalhammer A, Serna CG, Feehan O, Drawnel F, et al. Training and deploying a deep learning model for endoscopic severity grading in ulcerative colitis using multicenter clinical trial data. Therapeutic Advances in Gastrointestinal Endoscopy. 2021;14:2631774521990623. Lo et al. [2021] Lo B, Liu Z, Bendtsen F, Igel C, Vind I, Burisch TPP Johan. OP07 Artificial intelligence surpasses gastrointestinal experts in the classification of endoscopic severity among Ulcerative Colitis. Journal of Crohn’s and Colitis. 2021 05;15(Supplement):S007–S007. Vaze et al. [2022] Vaze S, Han K, Vedaldi A, Zisserman A. Open-set recognition: A good closed-set classifier is all you need? In: International Conference on Learning Representations (ICLR); 2022. . Gottlieb et al. [2021] Gottlieb K, Requa J, Karnes W, Chandra Gudivada R, Shen J, Rael E, et al. Central Reading of Ulcerative Colitis Clinical Trial Videos Using Neural Networks. Gastroenterology. 2021;160(3):710–719.e2. Woo et al. [2023] Woo S, Debnath S, Hu R, Chen X, Liu Z, Kweon IS, et al. ConvNeXt V2: Co-designing and Scaling ConvNets with Masked Autoencoders. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR); 2023. p. 6133–16142. Deng et al. [2009] Deng J, Dong W, Socher R, Li LJ, Li K, Fei-Fei L. Imagenet: A large-scale hierarchical image database. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR); 2009. p. 248–255. Guo et al. [2017] Guo C, Pleiss G, Sun Y, Weinberger KQ. On Calibration of Modern Neural Networks. In: Precup O, Teh YW, editors. Proceedings of the 34th International Conference on Machine Learning. vol. 70 of Proceedings of Machine Learning Research. PMLR; 2017. p. 1321–1330. Kishi et al. [2022] Kishi M, Hirai F, Takatsu N, et al. A review on the current status and definitions of activity indices in inflammatory bowel disease: how to use indices for precise evaluation. Journal of Gastroenterology. 2022;14:246–266. Vashist N, Samaan M, Mosli M, Parker C, MacDonald J, Nelson S, et al. Endoscopic scoring indices for evaluation of disease activity in ulcerative colitis. Cohrane Database of Systematic Reviews. 2018;1(1):CD011450. Ali [2022] Ali S. Where do we stand in AI for endoscopic image analysis? Deciphering gaps and future directions. npj Digital Medicine. 2022;5(1):184. Lo and Burisch [2021] Lo B, Burisch J. Artificial intelligence assisted assessment of endoscopic disease activity in inflammatory bowel disease. Artificial Intelligence in Gastrointestinal Endoscopy. 2021;2(4):95–102. Lo et al. [2022] Lo B, Liu Z, Bendtsen F, Igel C, Vind I, Burisch J. High accuracy in classifying endoscopic severity in ulcerative colitis using convolutional neural network. The American Journal of Gastroenterology. 2022;117(11):1648–1654. Becker et al. [2021] Becker BG, Arcadu F, Thalhammer A, Serna CG, Feehan O, Drawnel F, et al. Training and deploying a deep learning model for endoscopic severity grading in ulcerative colitis using multicenter clinical trial data. Therapeutic Advances in Gastrointestinal Endoscopy. 2021;14:2631774521990623. Lo et al. [2021] Lo B, Liu Z, Bendtsen F, Igel C, Vind I, Burisch TPP Johan. OP07 Artificial intelligence surpasses gastrointestinal experts in the classification of endoscopic severity among Ulcerative Colitis. Journal of Crohn’s and Colitis. 2021 05;15(Supplement):S007–S007. Vaze et al. [2022] Vaze S, Han K, Vedaldi A, Zisserman A. Open-set recognition: A good closed-set classifier is all you need? In: International Conference on Learning Representations (ICLR); 2022. . Gottlieb et al. [2021] Gottlieb K, Requa J, Karnes W, Chandra Gudivada R, Shen J, Rael E, et al. Central Reading of Ulcerative Colitis Clinical Trial Videos Using Neural Networks. Gastroenterology. 2021;160(3):710–719.e2. Woo et al. [2023] Woo S, Debnath S, Hu R, Chen X, Liu Z, Kweon IS, et al. ConvNeXt V2: Co-designing and Scaling ConvNets with Masked Autoencoders. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR); 2023. p. 6133–16142. Deng et al. [2009] Deng J, Dong W, Socher R, Li LJ, Li K, Fei-Fei L. Imagenet: A large-scale hierarchical image database. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR); 2009. p. 248–255. Guo et al. [2017] Guo C, Pleiss G, Sun Y, Weinberger KQ. On Calibration of Modern Neural Networks. In: Precup O, Teh YW, editors. Proceedings of the 34th International Conference on Machine Learning. vol. 70 of Proceedings of Machine Learning Research. PMLR; 2017. p. 1321–1330. Kishi et al. [2022] Kishi M, Hirai F, Takatsu N, et al. A review on the current status and definitions of activity indices in inflammatory bowel disease: how to use indices for precise evaluation. Journal of Gastroenterology. 2022;14:246–266. Ali S. Where do we stand in AI for endoscopic image analysis? Deciphering gaps and future directions. npj Digital Medicine. 2022;5(1):184. Lo and Burisch [2021] Lo B, Burisch J. Artificial intelligence assisted assessment of endoscopic disease activity in inflammatory bowel disease. Artificial Intelligence in Gastrointestinal Endoscopy. 2021;2(4):95–102. Lo et al. [2022] Lo B, Liu Z, Bendtsen F, Igel C, Vind I, Burisch J. High accuracy in classifying endoscopic severity in ulcerative colitis using convolutional neural network. The American Journal of Gastroenterology. 2022;117(11):1648–1654. Becker et al. [2021] Becker BG, Arcadu F, Thalhammer A, Serna CG, Feehan O, Drawnel F, et al. Training and deploying a deep learning model for endoscopic severity grading in ulcerative colitis using multicenter clinical trial data. Therapeutic Advances in Gastrointestinal Endoscopy. 2021;14:2631774521990623. Lo et al. [2021] Lo B, Liu Z, Bendtsen F, Igel C, Vind I, Burisch TPP Johan. OP07 Artificial intelligence surpasses gastrointestinal experts in the classification of endoscopic severity among Ulcerative Colitis. Journal of Crohn’s and Colitis. 2021 05;15(Supplement):S007–S007. Vaze et al. [2022] Vaze S, Han K, Vedaldi A, Zisserman A. Open-set recognition: A good closed-set classifier is all you need? In: International Conference on Learning Representations (ICLR); 2022. . Gottlieb et al. [2021] Gottlieb K, Requa J, Karnes W, Chandra Gudivada R, Shen J, Rael E, et al. Central Reading of Ulcerative Colitis Clinical Trial Videos Using Neural Networks. Gastroenterology. 2021;160(3):710–719.e2. Woo et al. [2023] Woo S, Debnath S, Hu R, Chen X, Liu Z, Kweon IS, et al. ConvNeXt V2: Co-designing and Scaling ConvNets with Masked Autoencoders. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR); 2023. p. 6133–16142. Deng et al. [2009] Deng J, Dong W, Socher R, Li LJ, Li K, Fei-Fei L. Imagenet: A large-scale hierarchical image database. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR); 2009. p. 248–255. Guo et al. [2017] Guo C, Pleiss G, Sun Y, Weinberger KQ. On Calibration of Modern Neural Networks. In: Precup O, Teh YW, editors. Proceedings of the 34th International Conference on Machine Learning. vol. 70 of Proceedings of Machine Learning Research. PMLR; 2017. p. 1321–1330. Kishi et al. [2022] Kishi M, Hirai F, Takatsu N, et al. A review on the current status and definitions of activity indices in inflammatory bowel disease: how to use indices for precise evaluation. Journal of Gastroenterology. 2022;14:246–266. Lo B, Burisch J. Artificial intelligence assisted assessment of endoscopic disease activity in inflammatory bowel disease. Artificial Intelligence in Gastrointestinal Endoscopy. 2021;2(4):95–102. Lo et al. [2022] Lo B, Liu Z, Bendtsen F, Igel C, Vind I, Burisch J. High accuracy in classifying endoscopic severity in ulcerative colitis using convolutional neural network. The American Journal of Gastroenterology. 2022;117(11):1648–1654. Becker et al. [2021] Becker BG, Arcadu F, Thalhammer A, Serna CG, Feehan O, Drawnel F, et al. Training and deploying a deep learning model for endoscopic severity grading in ulcerative colitis using multicenter clinical trial data. Therapeutic Advances in Gastrointestinal Endoscopy. 2021;14:2631774521990623. Lo et al. [2021] Lo B, Liu Z, Bendtsen F, Igel C, Vind I, Burisch TPP Johan. OP07 Artificial intelligence surpasses gastrointestinal experts in the classification of endoscopic severity among Ulcerative Colitis. Journal of Crohn’s and Colitis. 2021 05;15(Supplement):S007–S007. Vaze et al. [2022] Vaze S, Han K, Vedaldi A, Zisserman A. Open-set recognition: A good closed-set classifier is all you need? In: International Conference on Learning Representations (ICLR); 2022. . Gottlieb et al. [2021] Gottlieb K, Requa J, Karnes W, Chandra Gudivada R, Shen J, Rael E, et al. Central Reading of Ulcerative Colitis Clinical Trial Videos Using Neural Networks. Gastroenterology. 2021;160(3):710–719.e2. Woo et al. [2023] Woo S, Debnath S, Hu R, Chen X, Liu Z, Kweon IS, et al. ConvNeXt V2: Co-designing and Scaling ConvNets with Masked Autoencoders. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR); 2023. p. 6133–16142. Deng et al. [2009] Deng J, Dong W, Socher R, Li LJ, Li K, Fei-Fei L. Imagenet: A large-scale hierarchical image database. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR); 2009. p. 248–255. Guo et al. [2017] Guo C, Pleiss G, Sun Y, Weinberger KQ. On Calibration of Modern Neural Networks. In: Precup O, Teh YW, editors. Proceedings of the 34th International Conference on Machine Learning. vol. 70 of Proceedings of Machine Learning Research. PMLR; 2017. p. 1321–1330. Kishi et al. [2022] Kishi M, Hirai F, Takatsu N, et al. A review on the current status and definitions of activity indices in inflammatory bowel disease: how to use indices for precise evaluation. Journal of Gastroenterology. 2022;14:246–266. Lo B, Liu Z, Bendtsen F, Igel C, Vind I, Burisch J. High accuracy in classifying endoscopic severity in ulcerative colitis using convolutional neural network. The American Journal of Gastroenterology. 2022;117(11):1648–1654. Becker et al. [2021] Becker BG, Arcadu F, Thalhammer A, Serna CG, Feehan O, Drawnel F, et al. Training and deploying a deep learning model for endoscopic severity grading in ulcerative colitis using multicenter clinical trial data. Therapeutic Advances in Gastrointestinal Endoscopy. 2021;14:2631774521990623. Lo et al. [2021] Lo B, Liu Z, Bendtsen F, Igel C, Vind I, Burisch TPP Johan. OP07 Artificial intelligence surpasses gastrointestinal experts in the classification of endoscopic severity among Ulcerative Colitis. Journal of Crohn’s and Colitis. 2021 05;15(Supplement):S007–S007. Vaze et al. [2022] Vaze S, Han K, Vedaldi A, Zisserman A. Open-set recognition: A good closed-set classifier is all you need? In: International Conference on Learning Representations (ICLR); 2022. . Gottlieb et al. [2021] Gottlieb K, Requa J, Karnes W, Chandra Gudivada R, Shen J, Rael E, et al. Central Reading of Ulcerative Colitis Clinical Trial Videos Using Neural Networks. Gastroenterology. 2021;160(3):710–719.e2. Woo et al. [2023] Woo S, Debnath S, Hu R, Chen X, Liu Z, Kweon IS, et al. ConvNeXt V2: Co-designing and Scaling ConvNets with Masked Autoencoders. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR); 2023. p. 6133–16142. Deng et al. [2009] Deng J, Dong W, Socher R, Li LJ, Li K, Fei-Fei L. Imagenet: A large-scale hierarchical image database. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR); 2009. p. 248–255. Guo et al. [2017] Guo C, Pleiss G, Sun Y, Weinberger KQ. On Calibration of Modern Neural Networks. In: Precup O, Teh YW, editors. Proceedings of the 34th International Conference on Machine Learning. vol. 70 of Proceedings of Machine Learning Research. PMLR; 2017. p. 1321–1330. Kishi et al. [2022] Kishi M, Hirai F, Takatsu N, et al. A review on the current status and definitions of activity indices in inflammatory bowel disease: how to use indices for precise evaluation. Journal of Gastroenterology. 2022;14:246–266. Becker BG, Arcadu F, Thalhammer A, Serna CG, Feehan O, Drawnel F, et al. Training and deploying a deep learning model for endoscopic severity grading in ulcerative colitis using multicenter clinical trial data. Therapeutic Advances in Gastrointestinal Endoscopy. 2021;14:2631774521990623. Lo et al. [2021] Lo B, Liu Z, Bendtsen F, Igel C, Vind I, Burisch TPP Johan. OP07 Artificial intelligence surpasses gastrointestinal experts in the classification of endoscopic severity among Ulcerative Colitis. Journal of Crohn’s and Colitis. 2021 05;15(Supplement):S007–S007. Vaze et al. [2022] Vaze S, Han K, Vedaldi A, Zisserman A. Open-set recognition: A good closed-set classifier is all you need? In: International Conference on Learning Representations (ICLR); 2022. . Gottlieb et al. [2021] Gottlieb K, Requa J, Karnes W, Chandra Gudivada R, Shen J, Rael E, et al. Central Reading of Ulcerative Colitis Clinical Trial Videos Using Neural Networks. Gastroenterology. 2021;160(3):710–719.e2. Woo et al. [2023] Woo S, Debnath S, Hu R, Chen X, Liu Z, Kweon IS, et al. ConvNeXt V2: Co-designing and Scaling ConvNets with Masked Autoencoders. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR); 2023. p. 6133–16142. Deng et al. [2009] Deng J, Dong W, Socher R, Li LJ, Li K, Fei-Fei L. Imagenet: A large-scale hierarchical image database. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR); 2009. p. 248–255. Guo et al. [2017] Guo C, Pleiss G, Sun Y, Weinberger KQ. On Calibration of Modern Neural Networks. In: Precup O, Teh YW, editors. Proceedings of the 34th International Conference on Machine Learning. vol. 70 of Proceedings of Machine Learning Research. PMLR; 2017. p. 1321–1330. Kishi et al. [2022] Kishi M, Hirai F, Takatsu N, et al. A review on the current status and definitions of activity indices in inflammatory bowel disease: how to use indices for precise evaluation. Journal of Gastroenterology. 2022;14:246–266. Lo B, Liu Z, Bendtsen F, Igel C, Vind I, Burisch TPP Johan. OP07 Artificial intelligence surpasses gastrointestinal experts in the classification of endoscopic severity among Ulcerative Colitis. Journal of Crohn’s and Colitis. 2021 05;15(Supplement):S007–S007. Vaze et al. [2022] Vaze S, Han K, Vedaldi A, Zisserman A. Open-set recognition: A good closed-set classifier is all you need? In: International Conference on Learning Representations (ICLR); 2022. . Gottlieb et al. [2021] Gottlieb K, Requa J, Karnes W, Chandra Gudivada R, Shen J, Rael E, et al. Central Reading of Ulcerative Colitis Clinical Trial Videos Using Neural Networks. Gastroenterology. 2021;160(3):710–719.e2. Woo et al. [2023] Woo S, Debnath S, Hu R, Chen X, Liu Z, Kweon IS, et al. ConvNeXt V2: Co-designing and Scaling ConvNets with Masked Autoencoders. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR); 2023. p. 6133–16142. Deng et al. [2009] Deng J, Dong W, Socher R, Li LJ, Li K, Fei-Fei L. Imagenet: A large-scale hierarchical image database. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR); 2009. p. 248–255. Guo et al. [2017] Guo C, Pleiss G, Sun Y, Weinberger KQ. On Calibration of Modern Neural Networks. In: Precup O, Teh YW, editors. Proceedings of the 34th International Conference on Machine Learning. vol. 70 of Proceedings of Machine Learning Research. PMLR; 2017. p. 1321–1330. Kishi et al. [2022] Kishi M, Hirai F, Takatsu N, et al. A review on the current status and definitions of activity indices in inflammatory bowel disease: how to use indices for precise evaluation. Journal of Gastroenterology. 2022;14:246–266. Vaze S, Han K, Vedaldi A, Zisserman A. Open-set recognition: A good closed-set classifier is all you need? In: International Conference on Learning Representations (ICLR); 2022. . Gottlieb et al. [2021] Gottlieb K, Requa J, Karnes W, Chandra Gudivada R, Shen J, Rael E, et al. Central Reading of Ulcerative Colitis Clinical Trial Videos Using Neural Networks. Gastroenterology. 2021;160(3):710–719.e2. Woo et al. [2023] Woo S, Debnath S, Hu R, Chen X, Liu Z, Kweon IS, et al. ConvNeXt V2: Co-designing and Scaling ConvNets with Masked Autoencoders. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR); 2023. p. 6133–16142. Deng et al. [2009] Deng J, Dong W, Socher R, Li LJ, Li K, Fei-Fei L. Imagenet: A large-scale hierarchical image database. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR); 2009. p. 248–255. Guo et al. [2017] Guo C, Pleiss G, Sun Y, Weinberger KQ. On Calibration of Modern Neural Networks. In: Precup O, Teh YW, editors. Proceedings of the 34th International Conference on Machine Learning. vol. 70 of Proceedings of Machine Learning Research. PMLR; 2017. p. 1321–1330. Kishi et al. [2022] Kishi M, Hirai F, Takatsu N, et al. A review on the current status and definitions of activity indices in inflammatory bowel disease: how to use indices for precise evaluation. Journal of Gastroenterology. 2022;14:246–266. Gottlieb K, Requa J, Karnes W, Chandra Gudivada R, Shen J, Rael E, et al. Central Reading of Ulcerative Colitis Clinical Trial Videos Using Neural Networks. Gastroenterology. 2021;160(3):710–719.e2. Woo et al. [2023] Woo S, Debnath S, Hu R, Chen X, Liu Z, Kweon IS, et al. ConvNeXt V2: Co-designing and Scaling ConvNets with Masked Autoencoders. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR); 2023. p. 6133–16142. Deng et al. [2009] Deng J, Dong W, Socher R, Li LJ, Li K, Fei-Fei L. Imagenet: A large-scale hierarchical image database. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR); 2009. p. 248–255. Guo et al. [2017] Guo C, Pleiss G, Sun Y, Weinberger KQ. On Calibration of Modern Neural Networks. In: Precup O, Teh YW, editors. Proceedings of the 34th International Conference on Machine Learning. vol. 70 of Proceedings of Machine Learning Research. PMLR; 2017. p. 1321–1330. Kishi et al. [2022] Kishi M, Hirai F, Takatsu N, et al. A review on the current status and definitions of activity indices in inflammatory bowel disease: how to use indices for precise evaluation. Journal of Gastroenterology. 2022;14:246–266. Woo S, Debnath S, Hu R, Chen X, Liu Z, Kweon IS, et al. ConvNeXt V2: Co-designing and Scaling ConvNets with Masked Autoencoders. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR); 2023. p. 6133–16142. Deng et al. [2009] Deng J, Dong W, Socher R, Li LJ, Li K, Fei-Fei L. Imagenet: A large-scale hierarchical image database. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR); 2009. p. 248–255. Guo et al. [2017] Guo C, Pleiss G, Sun Y, Weinberger KQ. On Calibration of Modern Neural Networks. In: Precup O, Teh YW, editors. Proceedings of the 34th International Conference on Machine Learning. vol. 70 of Proceedings of Machine Learning Research. PMLR; 2017. p. 1321–1330. Kishi et al. [2022] Kishi M, Hirai F, Takatsu N, et al. A review on the current status and definitions of activity indices in inflammatory bowel disease: how to use indices for precise evaluation. Journal of Gastroenterology. 2022;14:246–266. Deng J, Dong W, Socher R, Li LJ, Li K, Fei-Fei L. Imagenet: A large-scale hierarchical image database. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR); 2009. p. 248–255. Guo et al. [2017] Guo C, Pleiss G, Sun Y, Weinberger KQ. On Calibration of Modern Neural Networks. In: Precup O, Teh YW, editors. Proceedings of the 34th International Conference on Machine Learning. vol. 70 of Proceedings of Machine Learning Research. PMLR; 2017. p. 1321–1330. Kishi et al. [2022] Kishi M, Hirai F, Takatsu N, et al. A review on the current status and definitions of activity indices in inflammatory bowel disease: how to use indices for precise evaluation. Journal of Gastroenterology. 2022;14:246–266. Guo C, Pleiss G, Sun Y, Weinberger KQ. On Calibration of Modern Neural Networks. In: Precup O, Teh YW, editors. Proceedings of the 34th International Conference on Machine Learning. vol. 70 of Proceedings of Machine Learning Research. PMLR; 2017. p. 1321–1330. Kishi et al. [2022] Kishi M, Hirai F, Takatsu N, et al. A review on the current status and definitions of activity indices in inflammatory bowel disease: how to use indices for precise evaluation. Journal of Gastroenterology. 2022;14:246–266. Kishi M, Hirai F, Takatsu N, et al. A review on the current status and definitions of activity indices in inflammatory bowel disease: how to use indices for precise evaluation. Journal of Gastroenterology. 2022;14:246–266.
- Endoscopic scoring indices for evaluation of disease activity in ulcerative colitis. Cohrane Database of Systematic Reviews. 2018;1(1):CD011450. Ali [2022] Ali S. Where do we stand in AI for endoscopic image analysis? Deciphering gaps and future directions. npj Digital Medicine. 2022;5(1):184. Lo and Burisch [2021] Lo B, Burisch J. Artificial intelligence assisted assessment of endoscopic disease activity in inflammatory bowel disease. Artificial Intelligence in Gastrointestinal Endoscopy. 2021;2(4):95–102. Lo et al. [2022] Lo B, Liu Z, Bendtsen F, Igel C, Vind I, Burisch J. High accuracy in classifying endoscopic severity in ulcerative colitis using convolutional neural network. The American Journal of Gastroenterology. 2022;117(11):1648–1654. Becker et al. [2021] Becker BG, Arcadu F, Thalhammer A, Serna CG, Feehan O, Drawnel F, et al. Training and deploying a deep learning model for endoscopic severity grading in ulcerative colitis using multicenter clinical trial data. Therapeutic Advances in Gastrointestinal Endoscopy. 2021;14:2631774521990623. Lo et al. [2021] Lo B, Liu Z, Bendtsen F, Igel C, Vind I, Burisch TPP Johan. OP07 Artificial intelligence surpasses gastrointestinal experts in the classification of endoscopic severity among Ulcerative Colitis. Journal of Crohn’s and Colitis. 2021 05;15(Supplement):S007–S007. Vaze et al. [2022] Vaze S, Han K, Vedaldi A, Zisserman A. Open-set recognition: A good closed-set classifier is all you need? In: International Conference on Learning Representations (ICLR); 2022. . Gottlieb et al. [2021] Gottlieb K, Requa J, Karnes W, Chandra Gudivada R, Shen J, Rael E, et al. Central Reading of Ulcerative Colitis Clinical Trial Videos Using Neural Networks. Gastroenterology. 2021;160(3):710–719.e2. Woo et al. [2023] Woo S, Debnath S, Hu R, Chen X, Liu Z, Kweon IS, et al. ConvNeXt V2: Co-designing and Scaling ConvNets with Masked Autoencoders. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR); 2023. p. 6133–16142. Deng et al. [2009] Deng J, Dong W, Socher R, Li LJ, Li K, Fei-Fei L. Imagenet: A large-scale hierarchical image database. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR); 2009. p. 248–255. Guo et al. [2017] Guo C, Pleiss G, Sun Y, Weinberger KQ. On Calibration of Modern Neural Networks. In: Precup O, Teh YW, editors. Proceedings of the 34th International Conference on Machine Learning. vol. 70 of Proceedings of Machine Learning Research. PMLR; 2017. p. 1321–1330. Kishi et al. [2022] Kishi M, Hirai F, Takatsu N, et al. A review on the current status and definitions of activity indices in inflammatory bowel disease: how to use indices for precise evaluation. Journal of Gastroenterology. 2022;14:246–266. Ali S. Where do we stand in AI for endoscopic image analysis? Deciphering gaps and future directions. npj Digital Medicine. 2022;5(1):184. Lo and Burisch [2021] Lo B, Burisch J. Artificial intelligence assisted assessment of endoscopic disease activity in inflammatory bowel disease. Artificial Intelligence in Gastrointestinal Endoscopy. 2021;2(4):95–102. Lo et al. [2022] Lo B, Liu Z, Bendtsen F, Igel C, Vind I, Burisch J. High accuracy in classifying endoscopic severity in ulcerative colitis using convolutional neural network. The American Journal of Gastroenterology. 2022;117(11):1648–1654. Becker et al. [2021] Becker BG, Arcadu F, Thalhammer A, Serna CG, Feehan O, Drawnel F, et al. Training and deploying a deep learning model for endoscopic severity grading in ulcerative colitis using multicenter clinical trial data. Therapeutic Advances in Gastrointestinal Endoscopy. 2021;14:2631774521990623. Lo et al. [2021] Lo B, Liu Z, Bendtsen F, Igel C, Vind I, Burisch TPP Johan. OP07 Artificial intelligence surpasses gastrointestinal experts in the classification of endoscopic severity among Ulcerative Colitis. Journal of Crohn’s and Colitis. 2021 05;15(Supplement):S007–S007. Vaze et al. [2022] Vaze S, Han K, Vedaldi A, Zisserman A. Open-set recognition: A good closed-set classifier is all you need? In: International Conference on Learning Representations (ICLR); 2022. . Gottlieb et al. [2021] Gottlieb K, Requa J, Karnes W, Chandra Gudivada R, Shen J, Rael E, et al. Central Reading of Ulcerative Colitis Clinical Trial Videos Using Neural Networks. Gastroenterology. 2021;160(3):710–719.e2. Woo et al. [2023] Woo S, Debnath S, Hu R, Chen X, Liu Z, Kweon IS, et al. ConvNeXt V2: Co-designing and Scaling ConvNets with Masked Autoencoders. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR); 2023. p. 6133–16142. Deng et al. [2009] Deng J, Dong W, Socher R, Li LJ, Li K, Fei-Fei L. Imagenet: A large-scale hierarchical image database. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR); 2009. p. 248–255. Guo et al. [2017] Guo C, Pleiss G, Sun Y, Weinberger KQ. On Calibration of Modern Neural Networks. In: Precup O, Teh YW, editors. Proceedings of the 34th International Conference on Machine Learning. vol. 70 of Proceedings of Machine Learning Research. PMLR; 2017. p. 1321–1330. Kishi et al. [2022] Kishi M, Hirai F, Takatsu N, et al. A review on the current status and definitions of activity indices in inflammatory bowel disease: how to use indices for precise evaluation. Journal of Gastroenterology. 2022;14:246–266. Lo B, Burisch J. Artificial intelligence assisted assessment of endoscopic disease activity in inflammatory bowel disease. Artificial Intelligence in Gastrointestinal Endoscopy. 2021;2(4):95–102. Lo et al. [2022] Lo B, Liu Z, Bendtsen F, Igel C, Vind I, Burisch J. High accuracy in classifying endoscopic severity in ulcerative colitis using convolutional neural network. The American Journal of Gastroenterology. 2022;117(11):1648–1654. Becker et al. [2021] Becker BG, Arcadu F, Thalhammer A, Serna CG, Feehan O, Drawnel F, et al. Training and deploying a deep learning model for endoscopic severity grading in ulcerative colitis using multicenter clinical trial data. Therapeutic Advances in Gastrointestinal Endoscopy. 2021;14:2631774521990623. Lo et al. [2021] Lo B, Liu Z, Bendtsen F, Igel C, Vind I, Burisch TPP Johan. OP07 Artificial intelligence surpasses gastrointestinal experts in the classification of endoscopic severity among Ulcerative Colitis. Journal of Crohn’s and Colitis. 2021 05;15(Supplement):S007–S007. Vaze et al. [2022] Vaze S, Han K, Vedaldi A, Zisserman A. Open-set recognition: A good closed-set classifier is all you need? In: International Conference on Learning Representations (ICLR); 2022. . Gottlieb et al. [2021] Gottlieb K, Requa J, Karnes W, Chandra Gudivada R, Shen J, Rael E, et al. Central Reading of Ulcerative Colitis Clinical Trial Videos Using Neural Networks. Gastroenterology. 2021;160(3):710–719.e2. Woo et al. [2023] Woo S, Debnath S, Hu R, Chen X, Liu Z, Kweon IS, et al. ConvNeXt V2: Co-designing and Scaling ConvNets with Masked Autoencoders. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR); 2023. p. 6133–16142. Deng et al. [2009] Deng J, Dong W, Socher R, Li LJ, Li K, Fei-Fei L. Imagenet: A large-scale hierarchical image database. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR); 2009. p. 248–255. Guo et al. [2017] Guo C, Pleiss G, Sun Y, Weinberger KQ. On Calibration of Modern Neural Networks. In: Precup O, Teh YW, editors. Proceedings of the 34th International Conference on Machine Learning. vol. 70 of Proceedings of Machine Learning Research. PMLR; 2017. p. 1321–1330. Kishi et al. [2022] Kishi M, Hirai F, Takatsu N, et al. A review on the current status and definitions of activity indices in inflammatory bowel disease: how to use indices for precise evaluation. Journal of Gastroenterology. 2022;14:246–266. Lo B, Liu Z, Bendtsen F, Igel C, Vind I, Burisch J. High accuracy in classifying endoscopic severity in ulcerative colitis using convolutional neural network. The American Journal of Gastroenterology. 2022;117(11):1648–1654. Becker et al. [2021] Becker BG, Arcadu F, Thalhammer A, Serna CG, Feehan O, Drawnel F, et al. Training and deploying a deep learning model for endoscopic severity grading in ulcerative colitis using multicenter clinical trial data. Therapeutic Advances in Gastrointestinal Endoscopy. 2021;14:2631774521990623. Lo et al. [2021] Lo B, Liu Z, Bendtsen F, Igel C, Vind I, Burisch TPP Johan. OP07 Artificial intelligence surpasses gastrointestinal experts in the classification of endoscopic severity among Ulcerative Colitis. Journal of Crohn’s and Colitis. 2021 05;15(Supplement):S007–S007. Vaze et al. [2022] Vaze S, Han K, Vedaldi A, Zisserman A. Open-set recognition: A good closed-set classifier is all you need? In: International Conference on Learning Representations (ICLR); 2022. . Gottlieb et al. [2021] Gottlieb K, Requa J, Karnes W, Chandra Gudivada R, Shen J, Rael E, et al. Central Reading of Ulcerative Colitis Clinical Trial Videos Using Neural Networks. Gastroenterology. 2021;160(3):710–719.e2. Woo et al. [2023] Woo S, Debnath S, Hu R, Chen X, Liu Z, Kweon IS, et al. ConvNeXt V2: Co-designing and Scaling ConvNets with Masked Autoencoders. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR); 2023. p. 6133–16142. Deng et al. [2009] Deng J, Dong W, Socher R, Li LJ, Li K, Fei-Fei L. Imagenet: A large-scale hierarchical image database. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR); 2009. p. 248–255. Guo et al. [2017] Guo C, Pleiss G, Sun Y, Weinberger KQ. On Calibration of Modern Neural Networks. In: Precup O, Teh YW, editors. Proceedings of the 34th International Conference on Machine Learning. vol. 70 of Proceedings of Machine Learning Research. PMLR; 2017. p. 1321–1330. Kishi et al. [2022] Kishi M, Hirai F, Takatsu N, et al. A review on the current status and definitions of activity indices in inflammatory bowel disease: how to use indices for precise evaluation. Journal of Gastroenterology. 2022;14:246–266. Becker BG, Arcadu F, Thalhammer A, Serna CG, Feehan O, Drawnel F, et al. Training and deploying a deep learning model for endoscopic severity grading in ulcerative colitis using multicenter clinical trial data. Therapeutic Advances in Gastrointestinal Endoscopy. 2021;14:2631774521990623. Lo et al. [2021] Lo B, Liu Z, Bendtsen F, Igel C, Vind I, Burisch TPP Johan. OP07 Artificial intelligence surpasses gastrointestinal experts in the classification of endoscopic severity among Ulcerative Colitis. Journal of Crohn’s and Colitis. 2021 05;15(Supplement):S007–S007. Vaze et al. [2022] Vaze S, Han K, Vedaldi A, Zisserman A. Open-set recognition: A good closed-set classifier is all you need? In: International Conference on Learning Representations (ICLR); 2022. . Gottlieb et al. [2021] Gottlieb K, Requa J, Karnes W, Chandra Gudivada R, Shen J, Rael E, et al. Central Reading of Ulcerative Colitis Clinical Trial Videos Using Neural Networks. Gastroenterology. 2021;160(3):710–719.e2. Woo et al. [2023] Woo S, Debnath S, Hu R, Chen X, Liu Z, Kweon IS, et al. ConvNeXt V2: Co-designing and Scaling ConvNets with Masked Autoencoders. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR); 2023. p. 6133–16142. Deng et al. [2009] Deng J, Dong W, Socher R, Li LJ, Li K, Fei-Fei L. Imagenet: A large-scale hierarchical image database. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR); 2009. p. 248–255. Guo et al. [2017] Guo C, Pleiss G, Sun Y, Weinberger KQ. On Calibration of Modern Neural Networks. In: Precup O, Teh YW, editors. Proceedings of the 34th International Conference on Machine Learning. vol. 70 of Proceedings of Machine Learning Research. PMLR; 2017. p. 1321–1330. Kishi et al. [2022] Kishi M, Hirai F, Takatsu N, et al. A review on the current status and definitions of activity indices in inflammatory bowel disease: how to use indices for precise evaluation. Journal of Gastroenterology. 2022;14:246–266. Lo B, Liu Z, Bendtsen F, Igel C, Vind I, Burisch TPP Johan. OP07 Artificial intelligence surpasses gastrointestinal experts in the classification of endoscopic severity among Ulcerative Colitis. Journal of Crohn’s and Colitis. 2021 05;15(Supplement):S007–S007. Vaze et al. [2022] Vaze S, Han K, Vedaldi A, Zisserman A. Open-set recognition: A good closed-set classifier is all you need? In: International Conference on Learning Representations (ICLR); 2022. . Gottlieb et al. [2021] Gottlieb K, Requa J, Karnes W, Chandra Gudivada R, Shen J, Rael E, et al. Central Reading of Ulcerative Colitis Clinical Trial Videos Using Neural Networks. Gastroenterology. 2021;160(3):710–719.e2. Woo et al. [2023] Woo S, Debnath S, Hu R, Chen X, Liu Z, Kweon IS, et al. ConvNeXt V2: Co-designing and Scaling ConvNets with Masked Autoencoders. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR); 2023. p. 6133–16142. Deng et al. [2009] Deng J, Dong W, Socher R, Li LJ, Li K, Fei-Fei L. Imagenet: A large-scale hierarchical image database. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR); 2009. p. 248–255. Guo et al. [2017] Guo C, Pleiss G, Sun Y, Weinberger KQ. On Calibration of Modern Neural Networks. In: Precup O, Teh YW, editors. Proceedings of the 34th International Conference on Machine Learning. vol. 70 of Proceedings of Machine Learning Research. PMLR; 2017. p. 1321–1330. Kishi et al. [2022] Kishi M, Hirai F, Takatsu N, et al. A review on the current status and definitions of activity indices in inflammatory bowel disease: how to use indices for precise evaluation. Journal of Gastroenterology. 2022;14:246–266. Vaze S, Han K, Vedaldi A, Zisserman A. Open-set recognition: A good closed-set classifier is all you need? In: International Conference on Learning Representations (ICLR); 2022. . Gottlieb et al. [2021] Gottlieb K, Requa J, Karnes W, Chandra Gudivada R, Shen J, Rael E, et al. Central Reading of Ulcerative Colitis Clinical Trial Videos Using Neural Networks. Gastroenterology. 2021;160(3):710–719.e2. Woo et al. [2023] Woo S, Debnath S, Hu R, Chen X, Liu Z, Kweon IS, et al. ConvNeXt V2: Co-designing and Scaling ConvNets with Masked Autoencoders. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR); 2023. p. 6133–16142. Deng et al. [2009] Deng J, Dong W, Socher R, Li LJ, Li K, Fei-Fei L. Imagenet: A large-scale hierarchical image database. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR); 2009. p. 248–255. Guo et al. [2017] Guo C, Pleiss G, Sun Y, Weinberger KQ. On Calibration of Modern Neural Networks. In: Precup O, Teh YW, editors. Proceedings of the 34th International Conference on Machine Learning. vol. 70 of Proceedings of Machine Learning Research. PMLR; 2017. p. 1321–1330. Kishi et al. [2022] Kishi M, Hirai F, Takatsu N, et al. A review on the current status and definitions of activity indices in inflammatory bowel disease: how to use indices for precise evaluation. Journal of Gastroenterology. 2022;14:246–266. Gottlieb K, Requa J, Karnes W, Chandra Gudivada R, Shen J, Rael E, et al. Central Reading of Ulcerative Colitis Clinical Trial Videos Using Neural Networks. Gastroenterology. 2021;160(3):710–719.e2. Woo et al. [2023] Woo S, Debnath S, Hu R, Chen X, Liu Z, Kweon IS, et al. ConvNeXt V2: Co-designing and Scaling ConvNets with Masked Autoencoders. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR); 2023. p. 6133–16142. Deng et al. [2009] Deng J, Dong W, Socher R, Li LJ, Li K, Fei-Fei L. Imagenet: A large-scale hierarchical image database. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR); 2009. p. 248–255. Guo et al. [2017] Guo C, Pleiss G, Sun Y, Weinberger KQ. On Calibration of Modern Neural Networks. In: Precup O, Teh YW, editors. Proceedings of the 34th International Conference on Machine Learning. vol. 70 of Proceedings of Machine Learning Research. PMLR; 2017. p. 1321–1330. Kishi et al. [2022] Kishi M, Hirai F, Takatsu N, et al. A review on the current status and definitions of activity indices in inflammatory bowel disease: how to use indices for precise evaluation. Journal of Gastroenterology. 2022;14:246–266. Woo S, Debnath S, Hu R, Chen X, Liu Z, Kweon IS, et al. ConvNeXt V2: Co-designing and Scaling ConvNets with Masked Autoencoders. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR); 2023. p. 6133–16142. Deng et al. [2009] Deng J, Dong W, Socher R, Li LJ, Li K, Fei-Fei L. Imagenet: A large-scale hierarchical image database. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR); 2009. p. 248–255. Guo et al. [2017] Guo C, Pleiss G, Sun Y, Weinberger KQ. On Calibration of Modern Neural Networks. In: Precup O, Teh YW, editors. Proceedings of the 34th International Conference on Machine Learning. vol. 70 of Proceedings of Machine Learning Research. PMLR; 2017. p. 1321–1330. Kishi et al. [2022] Kishi M, Hirai F, Takatsu N, et al. A review on the current status and definitions of activity indices in inflammatory bowel disease: how to use indices for precise evaluation. Journal of Gastroenterology. 2022;14:246–266. Deng J, Dong W, Socher R, Li LJ, Li K, Fei-Fei L. Imagenet: A large-scale hierarchical image database. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR); 2009. p. 248–255. Guo et al. [2017] Guo C, Pleiss G, Sun Y, Weinberger KQ. On Calibration of Modern Neural Networks. In: Precup O, Teh YW, editors. Proceedings of the 34th International Conference on Machine Learning. vol. 70 of Proceedings of Machine Learning Research. PMLR; 2017. p. 1321–1330. Kishi et al. [2022] Kishi M, Hirai F, Takatsu N, et al. A review on the current status and definitions of activity indices in inflammatory bowel disease: how to use indices for precise evaluation. Journal of Gastroenterology. 2022;14:246–266. Guo C, Pleiss G, Sun Y, Weinberger KQ. On Calibration of Modern Neural Networks. In: Precup O, Teh YW, editors. Proceedings of the 34th International Conference on Machine Learning. vol. 70 of Proceedings of Machine Learning Research. PMLR; 2017. p. 1321–1330. Kishi et al. [2022] Kishi M, Hirai F, Takatsu N, et al. A review on the current status and definitions of activity indices in inflammatory bowel disease: how to use indices for precise evaluation. Journal of Gastroenterology. 2022;14:246–266. Kishi M, Hirai F, Takatsu N, et al. A review on the current status and definitions of activity indices in inflammatory bowel disease: how to use indices for precise evaluation. Journal of Gastroenterology. 2022;14:246–266.
- Ali S. Where do we stand in AI for endoscopic image analysis? Deciphering gaps and future directions. npj Digital Medicine. 2022;5(1):184. Lo and Burisch [2021] Lo B, Burisch J. Artificial intelligence assisted assessment of endoscopic disease activity in inflammatory bowel disease. Artificial Intelligence in Gastrointestinal Endoscopy. 2021;2(4):95–102. Lo et al. [2022] Lo B, Liu Z, Bendtsen F, Igel C, Vind I, Burisch J. High accuracy in classifying endoscopic severity in ulcerative colitis using convolutional neural network. The American Journal of Gastroenterology. 2022;117(11):1648–1654. Becker et al. [2021] Becker BG, Arcadu F, Thalhammer A, Serna CG, Feehan O, Drawnel F, et al. Training and deploying a deep learning model for endoscopic severity grading in ulcerative colitis using multicenter clinical trial data. Therapeutic Advances in Gastrointestinal Endoscopy. 2021;14:2631774521990623. Lo et al. [2021] Lo B, Liu Z, Bendtsen F, Igel C, Vind I, Burisch TPP Johan. OP07 Artificial intelligence surpasses gastrointestinal experts in the classification of endoscopic severity among Ulcerative Colitis. Journal of Crohn’s and Colitis. 2021 05;15(Supplement):S007–S007. Vaze et al. [2022] Vaze S, Han K, Vedaldi A, Zisserman A. Open-set recognition: A good closed-set classifier is all you need? In: International Conference on Learning Representations (ICLR); 2022. . Gottlieb et al. [2021] Gottlieb K, Requa J, Karnes W, Chandra Gudivada R, Shen J, Rael E, et al. Central Reading of Ulcerative Colitis Clinical Trial Videos Using Neural Networks. Gastroenterology. 2021;160(3):710–719.e2. Woo et al. [2023] Woo S, Debnath S, Hu R, Chen X, Liu Z, Kweon IS, et al. ConvNeXt V2: Co-designing and Scaling ConvNets with Masked Autoencoders. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR); 2023. p. 6133–16142. Deng et al. [2009] Deng J, Dong W, Socher R, Li LJ, Li K, Fei-Fei L. Imagenet: A large-scale hierarchical image database. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR); 2009. p. 248–255. Guo et al. [2017] Guo C, Pleiss G, Sun Y, Weinberger KQ. On Calibration of Modern Neural Networks. In: Precup O, Teh YW, editors. Proceedings of the 34th International Conference on Machine Learning. vol. 70 of Proceedings of Machine Learning Research. PMLR; 2017. p. 1321–1330. Kishi et al. [2022] Kishi M, Hirai F, Takatsu N, et al. A review on the current status and definitions of activity indices in inflammatory bowel disease: how to use indices for precise evaluation. Journal of Gastroenterology. 2022;14:246–266. Lo B, Burisch J. Artificial intelligence assisted assessment of endoscopic disease activity in inflammatory bowel disease. Artificial Intelligence in Gastrointestinal Endoscopy. 2021;2(4):95–102. Lo et al. [2022] Lo B, Liu Z, Bendtsen F, Igel C, Vind I, Burisch J. High accuracy in classifying endoscopic severity in ulcerative colitis using convolutional neural network. The American Journal of Gastroenterology. 2022;117(11):1648–1654. Becker et al. [2021] Becker BG, Arcadu F, Thalhammer A, Serna CG, Feehan O, Drawnel F, et al. Training and deploying a deep learning model for endoscopic severity grading in ulcerative colitis using multicenter clinical trial data. Therapeutic Advances in Gastrointestinal Endoscopy. 2021;14:2631774521990623. Lo et al. [2021] Lo B, Liu Z, Bendtsen F, Igel C, Vind I, Burisch TPP Johan. OP07 Artificial intelligence surpasses gastrointestinal experts in the classification of endoscopic severity among Ulcerative Colitis. Journal of Crohn’s and Colitis. 2021 05;15(Supplement):S007–S007. Vaze et al. [2022] Vaze S, Han K, Vedaldi A, Zisserman A. Open-set recognition: A good closed-set classifier is all you need? In: International Conference on Learning Representations (ICLR); 2022. . Gottlieb et al. [2021] Gottlieb K, Requa J, Karnes W, Chandra Gudivada R, Shen J, Rael E, et al. Central Reading of Ulcerative Colitis Clinical Trial Videos Using Neural Networks. Gastroenterology. 2021;160(3):710–719.e2. Woo et al. [2023] Woo S, Debnath S, Hu R, Chen X, Liu Z, Kweon IS, et al. ConvNeXt V2: Co-designing and Scaling ConvNets with Masked Autoencoders. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR); 2023. p. 6133–16142. Deng et al. [2009] Deng J, Dong W, Socher R, Li LJ, Li K, Fei-Fei L. Imagenet: A large-scale hierarchical image database. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR); 2009. p. 248–255. Guo et al. [2017] Guo C, Pleiss G, Sun Y, Weinberger KQ. On Calibration of Modern Neural Networks. In: Precup O, Teh YW, editors. Proceedings of the 34th International Conference on Machine Learning. vol. 70 of Proceedings of Machine Learning Research. PMLR; 2017. p. 1321–1330. Kishi et al. [2022] Kishi M, Hirai F, Takatsu N, et al. A review on the current status and definitions of activity indices in inflammatory bowel disease: how to use indices for precise evaluation. Journal of Gastroenterology. 2022;14:246–266. Lo B, Liu Z, Bendtsen F, Igel C, Vind I, Burisch J. High accuracy in classifying endoscopic severity in ulcerative colitis using convolutional neural network. The American Journal of Gastroenterology. 2022;117(11):1648–1654. Becker et al. [2021] Becker BG, Arcadu F, Thalhammer A, Serna CG, Feehan O, Drawnel F, et al. Training and deploying a deep learning model for endoscopic severity grading in ulcerative colitis using multicenter clinical trial data. Therapeutic Advances in Gastrointestinal Endoscopy. 2021;14:2631774521990623. Lo et al. [2021] Lo B, Liu Z, Bendtsen F, Igel C, Vind I, Burisch TPP Johan. OP07 Artificial intelligence surpasses gastrointestinal experts in the classification of endoscopic severity among Ulcerative Colitis. Journal of Crohn’s and Colitis. 2021 05;15(Supplement):S007–S007. Vaze et al. [2022] Vaze S, Han K, Vedaldi A, Zisserman A. Open-set recognition: A good closed-set classifier is all you need? In: International Conference on Learning Representations (ICLR); 2022. . Gottlieb et al. [2021] Gottlieb K, Requa J, Karnes W, Chandra Gudivada R, Shen J, Rael E, et al. Central Reading of Ulcerative Colitis Clinical Trial Videos Using Neural Networks. Gastroenterology. 2021;160(3):710–719.e2. Woo et al. [2023] Woo S, Debnath S, Hu R, Chen X, Liu Z, Kweon IS, et al. ConvNeXt V2: Co-designing and Scaling ConvNets with Masked Autoencoders. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR); 2023. p. 6133–16142. Deng et al. [2009] Deng J, Dong W, Socher R, Li LJ, Li K, Fei-Fei L. Imagenet: A large-scale hierarchical image database. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR); 2009. p. 248–255. Guo et al. [2017] Guo C, Pleiss G, Sun Y, Weinberger KQ. On Calibration of Modern Neural Networks. In: Precup O, Teh YW, editors. Proceedings of the 34th International Conference on Machine Learning. vol. 70 of Proceedings of Machine Learning Research. PMLR; 2017. p. 1321–1330. Kishi et al. [2022] Kishi M, Hirai F, Takatsu N, et al. A review on the current status and definitions of activity indices in inflammatory bowel disease: how to use indices for precise evaluation. Journal of Gastroenterology. 2022;14:246–266. Becker BG, Arcadu F, Thalhammer A, Serna CG, Feehan O, Drawnel F, et al. Training and deploying a deep learning model for endoscopic severity grading in ulcerative colitis using multicenter clinical trial data. Therapeutic Advances in Gastrointestinal Endoscopy. 2021;14:2631774521990623. Lo et al. [2021] Lo B, Liu Z, Bendtsen F, Igel C, Vind I, Burisch TPP Johan. OP07 Artificial intelligence surpasses gastrointestinal experts in the classification of endoscopic severity among Ulcerative Colitis. Journal of Crohn’s and Colitis. 2021 05;15(Supplement):S007–S007. Vaze et al. [2022] Vaze S, Han K, Vedaldi A, Zisserman A. Open-set recognition: A good closed-set classifier is all you need? In: International Conference on Learning Representations (ICLR); 2022. . Gottlieb et al. [2021] Gottlieb K, Requa J, Karnes W, Chandra Gudivada R, Shen J, Rael E, et al. Central Reading of Ulcerative Colitis Clinical Trial Videos Using Neural Networks. Gastroenterology. 2021;160(3):710–719.e2. Woo et al. [2023] Woo S, Debnath S, Hu R, Chen X, Liu Z, Kweon IS, et al. ConvNeXt V2: Co-designing and Scaling ConvNets with Masked Autoencoders. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR); 2023. p. 6133–16142. Deng et al. [2009] Deng J, Dong W, Socher R, Li LJ, Li K, Fei-Fei L. Imagenet: A large-scale hierarchical image database. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR); 2009. p. 248–255. Guo et al. [2017] Guo C, Pleiss G, Sun Y, Weinberger KQ. On Calibration of Modern Neural Networks. In: Precup O, Teh YW, editors. Proceedings of the 34th International Conference on Machine Learning. vol. 70 of Proceedings of Machine Learning Research. PMLR; 2017. p. 1321–1330. Kishi et al. [2022] Kishi M, Hirai F, Takatsu N, et al. A review on the current status and definitions of activity indices in inflammatory bowel disease: how to use indices for precise evaluation. Journal of Gastroenterology. 2022;14:246–266. Lo B, Liu Z, Bendtsen F, Igel C, Vind I, Burisch TPP Johan. OP07 Artificial intelligence surpasses gastrointestinal experts in the classification of endoscopic severity among Ulcerative Colitis. Journal of Crohn’s and Colitis. 2021 05;15(Supplement):S007–S007. Vaze et al. [2022] Vaze S, Han K, Vedaldi A, Zisserman A. Open-set recognition: A good closed-set classifier is all you need? In: International Conference on Learning Representations (ICLR); 2022. . Gottlieb et al. [2021] Gottlieb K, Requa J, Karnes W, Chandra Gudivada R, Shen J, Rael E, et al. Central Reading of Ulcerative Colitis Clinical Trial Videos Using Neural Networks. Gastroenterology. 2021;160(3):710–719.e2. Woo et al. [2023] Woo S, Debnath S, Hu R, Chen X, Liu Z, Kweon IS, et al. ConvNeXt V2: Co-designing and Scaling ConvNets with Masked Autoencoders. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR); 2023. p. 6133–16142. Deng et al. [2009] Deng J, Dong W, Socher R, Li LJ, Li K, Fei-Fei L. Imagenet: A large-scale hierarchical image database. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR); 2009. p. 248–255. Guo et al. [2017] Guo C, Pleiss G, Sun Y, Weinberger KQ. On Calibration of Modern Neural Networks. In: Precup O, Teh YW, editors. Proceedings of the 34th International Conference on Machine Learning. vol. 70 of Proceedings of Machine Learning Research. PMLR; 2017. p. 1321–1330. Kishi et al. [2022] Kishi M, Hirai F, Takatsu N, et al. A review on the current status and definitions of activity indices in inflammatory bowel disease: how to use indices for precise evaluation. Journal of Gastroenterology. 2022;14:246–266. Vaze S, Han K, Vedaldi A, Zisserman A. Open-set recognition: A good closed-set classifier is all you need? In: International Conference on Learning Representations (ICLR); 2022. . Gottlieb et al. [2021] Gottlieb K, Requa J, Karnes W, Chandra Gudivada R, Shen J, Rael E, et al. Central Reading of Ulcerative Colitis Clinical Trial Videos Using Neural Networks. Gastroenterology. 2021;160(3):710–719.e2. Woo et al. [2023] Woo S, Debnath S, Hu R, Chen X, Liu Z, Kweon IS, et al. ConvNeXt V2: Co-designing and Scaling ConvNets with Masked Autoencoders. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR); 2023. p. 6133–16142. Deng et al. [2009] Deng J, Dong W, Socher R, Li LJ, Li K, Fei-Fei L. Imagenet: A large-scale hierarchical image database. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR); 2009. p. 248–255. Guo et al. [2017] Guo C, Pleiss G, Sun Y, Weinberger KQ. On Calibration of Modern Neural Networks. In: Precup O, Teh YW, editors. Proceedings of the 34th International Conference on Machine Learning. vol. 70 of Proceedings of Machine Learning Research. PMLR; 2017. p. 1321–1330. Kishi et al. [2022] Kishi M, Hirai F, Takatsu N, et al. A review on the current status and definitions of activity indices in inflammatory bowel disease: how to use indices for precise evaluation. Journal of Gastroenterology. 2022;14:246–266. Gottlieb K, Requa J, Karnes W, Chandra Gudivada R, Shen J, Rael E, et al. Central Reading of Ulcerative Colitis Clinical Trial Videos Using Neural Networks. Gastroenterology. 2021;160(3):710–719.e2. Woo et al. [2023] Woo S, Debnath S, Hu R, Chen X, Liu Z, Kweon IS, et al. ConvNeXt V2: Co-designing and Scaling ConvNets with Masked Autoencoders. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR); 2023. p. 6133–16142. Deng et al. [2009] Deng J, Dong W, Socher R, Li LJ, Li K, Fei-Fei L. Imagenet: A large-scale hierarchical image database. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR); 2009. p. 248–255. Guo et al. [2017] Guo C, Pleiss G, Sun Y, Weinberger KQ. On Calibration of Modern Neural Networks. In: Precup O, Teh YW, editors. Proceedings of the 34th International Conference on Machine Learning. vol. 70 of Proceedings of Machine Learning Research. PMLR; 2017. p. 1321–1330. Kishi et al. [2022] Kishi M, Hirai F, Takatsu N, et al. A review on the current status and definitions of activity indices in inflammatory bowel disease: how to use indices for precise evaluation. Journal of Gastroenterology. 2022;14:246–266. Woo S, Debnath S, Hu R, Chen X, Liu Z, Kweon IS, et al. ConvNeXt V2: Co-designing and Scaling ConvNets with Masked Autoencoders. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR); 2023. p. 6133–16142. Deng et al. [2009] Deng J, Dong W, Socher R, Li LJ, Li K, Fei-Fei L. Imagenet: A large-scale hierarchical image database. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR); 2009. p. 248–255. Guo et al. [2017] Guo C, Pleiss G, Sun Y, Weinberger KQ. On Calibration of Modern Neural Networks. In: Precup O, Teh YW, editors. Proceedings of the 34th International Conference on Machine Learning. vol. 70 of Proceedings of Machine Learning Research. PMLR; 2017. p. 1321–1330. Kishi et al. [2022] Kishi M, Hirai F, Takatsu N, et al. A review on the current status and definitions of activity indices in inflammatory bowel disease: how to use indices for precise evaluation. Journal of Gastroenterology. 2022;14:246–266. Deng J, Dong W, Socher R, Li LJ, Li K, Fei-Fei L. Imagenet: A large-scale hierarchical image database. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR); 2009. p. 248–255. Guo et al. [2017] Guo C, Pleiss G, Sun Y, Weinberger KQ. On Calibration of Modern Neural Networks. In: Precup O, Teh YW, editors. Proceedings of the 34th International Conference on Machine Learning. vol. 70 of Proceedings of Machine Learning Research. PMLR; 2017. p. 1321–1330. Kishi et al. [2022] Kishi M, Hirai F, Takatsu N, et al. A review on the current status and definitions of activity indices in inflammatory bowel disease: how to use indices for precise evaluation. Journal of Gastroenterology. 2022;14:246–266. Guo C, Pleiss G, Sun Y, Weinberger KQ. On Calibration of Modern Neural Networks. In: Precup O, Teh YW, editors. Proceedings of the 34th International Conference on Machine Learning. vol. 70 of Proceedings of Machine Learning Research. PMLR; 2017. p. 1321–1330. Kishi et al. [2022] Kishi M, Hirai F, Takatsu N, et al. A review on the current status and definitions of activity indices in inflammatory bowel disease: how to use indices for precise evaluation. Journal of Gastroenterology. 2022;14:246–266. Kishi M, Hirai F, Takatsu N, et al. A review on the current status and definitions of activity indices in inflammatory bowel disease: how to use indices for precise evaluation. Journal of Gastroenterology. 2022;14:246–266.
- Lo B, Burisch J. Artificial intelligence assisted assessment of endoscopic disease activity in inflammatory bowel disease. Artificial Intelligence in Gastrointestinal Endoscopy. 2021;2(4):95–102. Lo et al. [2022] Lo B, Liu Z, Bendtsen F, Igel C, Vind I, Burisch J. High accuracy in classifying endoscopic severity in ulcerative colitis using convolutional neural network. The American Journal of Gastroenterology. 2022;117(11):1648–1654. Becker et al. [2021] Becker BG, Arcadu F, Thalhammer A, Serna CG, Feehan O, Drawnel F, et al. Training and deploying a deep learning model for endoscopic severity grading in ulcerative colitis using multicenter clinical trial data. Therapeutic Advances in Gastrointestinal Endoscopy. 2021;14:2631774521990623. Lo et al. [2021] Lo B, Liu Z, Bendtsen F, Igel C, Vind I, Burisch TPP Johan. OP07 Artificial intelligence surpasses gastrointestinal experts in the classification of endoscopic severity among Ulcerative Colitis. Journal of Crohn’s and Colitis. 2021 05;15(Supplement):S007–S007. Vaze et al. [2022] Vaze S, Han K, Vedaldi A, Zisserman A. Open-set recognition: A good closed-set classifier is all you need? In: International Conference on Learning Representations (ICLR); 2022. . Gottlieb et al. [2021] Gottlieb K, Requa J, Karnes W, Chandra Gudivada R, Shen J, Rael E, et al. Central Reading of Ulcerative Colitis Clinical Trial Videos Using Neural Networks. Gastroenterology. 2021;160(3):710–719.e2. Woo et al. [2023] Woo S, Debnath S, Hu R, Chen X, Liu Z, Kweon IS, et al. ConvNeXt V2: Co-designing and Scaling ConvNets with Masked Autoencoders. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR); 2023. p. 6133–16142. Deng et al. [2009] Deng J, Dong W, Socher R, Li LJ, Li K, Fei-Fei L. Imagenet: A large-scale hierarchical image database. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR); 2009. p. 248–255. Guo et al. [2017] Guo C, Pleiss G, Sun Y, Weinberger KQ. On Calibration of Modern Neural Networks. In: Precup O, Teh YW, editors. Proceedings of the 34th International Conference on Machine Learning. vol. 70 of Proceedings of Machine Learning Research. PMLR; 2017. p. 1321–1330. Kishi et al. [2022] Kishi M, Hirai F, Takatsu N, et al. A review on the current status and definitions of activity indices in inflammatory bowel disease: how to use indices for precise evaluation. Journal of Gastroenterology. 2022;14:246–266. Lo B, Liu Z, Bendtsen F, Igel C, Vind I, Burisch J. High accuracy in classifying endoscopic severity in ulcerative colitis using convolutional neural network. The American Journal of Gastroenterology. 2022;117(11):1648–1654. Becker et al. [2021] Becker BG, Arcadu F, Thalhammer A, Serna CG, Feehan O, Drawnel F, et al. Training and deploying a deep learning model for endoscopic severity grading in ulcerative colitis using multicenter clinical trial data. Therapeutic Advances in Gastrointestinal Endoscopy. 2021;14:2631774521990623. Lo et al. [2021] Lo B, Liu Z, Bendtsen F, Igel C, Vind I, Burisch TPP Johan. OP07 Artificial intelligence surpasses gastrointestinal experts in the classification of endoscopic severity among Ulcerative Colitis. Journal of Crohn’s and Colitis. 2021 05;15(Supplement):S007–S007. Vaze et al. [2022] Vaze S, Han K, Vedaldi A, Zisserman A. Open-set recognition: A good closed-set classifier is all you need? In: International Conference on Learning Representations (ICLR); 2022. . Gottlieb et al. [2021] Gottlieb K, Requa J, Karnes W, Chandra Gudivada R, Shen J, Rael E, et al. Central Reading of Ulcerative Colitis Clinical Trial Videos Using Neural Networks. Gastroenterology. 2021;160(3):710–719.e2. Woo et al. [2023] Woo S, Debnath S, Hu R, Chen X, Liu Z, Kweon IS, et al. ConvNeXt V2: Co-designing and Scaling ConvNets with Masked Autoencoders. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR); 2023. p. 6133–16142. Deng et al. [2009] Deng J, Dong W, Socher R, Li LJ, Li K, Fei-Fei L. Imagenet: A large-scale hierarchical image database. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR); 2009. p. 248–255. Guo et al. [2017] Guo C, Pleiss G, Sun Y, Weinberger KQ. On Calibration of Modern Neural Networks. In: Precup O, Teh YW, editors. Proceedings of the 34th International Conference on Machine Learning. vol. 70 of Proceedings of Machine Learning Research. PMLR; 2017. p. 1321–1330. Kishi et al. [2022] Kishi M, Hirai F, Takatsu N, et al. A review on the current status and definitions of activity indices in inflammatory bowel disease: how to use indices for precise evaluation. Journal of Gastroenterology. 2022;14:246–266. Becker BG, Arcadu F, Thalhammer A, Serna CG, Feehan O, Drawnel F, et al. Training and deploying a deep learning model for endoscopic severity grading in ulcerative colitis using multicenter clinical trial data. Therapeutic Advances in Gastrointestinal Endoscopy. 2021;14:2631774521990623. Lo et al. [2021] Lo B, Liu Z, Bendtsen F, Igel C, Vind I, Burisch TPP Johan. OP07 Artificial intelligence surpasses gastrointestinal experts in the classification of endoscopic severity among Ulcerative Colitis. Journal of Crohn’s and Colitis. 2021 05;15(Supplement):S007–S007. Vaze et al. [2022] Vaze S, Han K, Vedaldi A, Zisserman A. Open-set recognition: A good closed-set classifier is all you need? In: International Conference on Learning Representations (ICLR); 2022. . Gottlieb et al. [2021] Gottlieb K, Requa J, Karnes W, Chandra Gudivada R, Shen J, Rael E, et al. Central Reading of Ulcerative Colitis Clinical Trial Videos Using Neural Networks. Gastroenterology. 2021;160(3):710–719.e2. Woo et al. [2023] Woo S, Debnath S, Hu R, Chen X, Liu Z, Kweon IS, et al. ConvNeXt V2: Co-designing and Scaling ConvNets with Masked Autoencoders. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR); 2023. p. 6133–16142. Deng et al. [2009] Deng J, Dong W, Socher R, Li LJ, Li K, Fei-Fei L. Imagenet: A large-scale hierarchical image database. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR); 2009. p. 248–255. Guo et al. [2017] Guo C, Pleiss G, Sun Y, Weinberger KQ. On Calibration of Modern Neural Networks. In: Precup O, Teh YW, editors. Proceedings of the 34th International Conference on Machine Learning. vol. 70 of Proceedings of Machine Learning Research. PMLR; 2017. p. 1321–1330. Kishi et al. [2022] Kishi M, Hirai F, Takatsu N, et al. A review on the current status and definitions of activity indices in inflammatory bowel disease: how to use indices for precise evaluation. Journal of Gastroenterology. 2022;14:246–266. Lo B, Liu Z, Bendtsen F, Igel C, Vind I, Burisch TPP Johan. OP07 Artificial intelligence surpasses gastrointestinal experts in the classification of endoscopic severity among Ulcerative Colitis. Journal of Crohn’s and Colitis. 2021 05;15(Supplement):S007–S007. Vaze et al. [2022] Vaze S, Han K, Vedaldi A, Zisserman A. Open-set recognition: A good closed-set classifier is all you need? In: International Conference on Learning Representations (ICLR); 2022. . Gottlieb et al. [2021] Gottlieb K, Requa J, Karnes W, Chandra Gudivada R, Shen J, Rael E, et al. Central Reading of Ulcerative Colitis Clinical Trial Videos Using Neural Networks. Gastroenterology. 2021;160(3):710–719.e2. Woo et al. [2023] Woo S, Debnath S, Hu R, Chen X, Liu Z, Kweon IS, et al. ConvNeXt V2: Co-designing and Scaling ConvNets with Masked Autoencoders. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR); 2023. p. 6133–16142. Deng et al. [2009] Deng J, Dong W, Socher R, Li LJ, Li K, Fei-Fei L. Imagenet: A large-scale hierarchical image database. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR); 2009. p. 248–255. Guo et al. [2017] Guo C, Pleiss G, Sun Y, Weinberger KQ. On Calibration of Modern Neural Networks. In: Precup O, Teh YW, editors. Proceedings of the 34th International Conference on Machine Learning. vol. 70 of Proceedings of Machine Learning Research. PMLR; 2017. p. 1321–1330. Kishi et al. [2022] Kishi M, Hirai F, Takatsu N, et al. A review on the current status and definitions of activity indices in inflammatory bowel disease: how to use indices for precise evaluation. Journal of Gastroenterology. 2022;14:246–266. Vaze S, Han K, Vedaldi A, Zisserman A. Open-set recognition: A good closed-set classifier is all you need? In: International Conference on Learning Representations (ICLR); 2022. . Gottlieb et al. [2021] Gottlieb K, Requa J, Karnes W, Chandra Gudivada R, Shen J, Rael E, et al. Central Reading of Ulcerative Colitis Clinical Trial Videos Using Neural Networks. Gastroenterology. 2021;160(3):710–719.e2. Woo et al. [2023] Woo S, Debnath S, Hu R, Chen X, Liu Z, Kweon IS, et al. ConvNeXt V2: Co-designing and Scaling ConvNets with Masked Autoencoders. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR); 2023. p. 6133–16142. Deng et al. [2009] Deng J, Dong W, Socher R, Li LJ, Li K, Fei-Fei L. Imagenet: A large-scale hierarchical image database. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR); 2009. p. 248–255. Guo et al. [2017] Guo C, Pleiss G, Sun Y, Weinberger KQ. On Calibration of Modern Neural Networks. In: Precup O, Teh YW, editors. Proceedings of the 34th International Conference on Machine Learning. vol. 70 of Proceedings of Machine Learning Research. PMLR; 2017. p. 1321–1330. Kishi et al. [2022] Kishi M, Hirai F, Takatsu N, et al. A review on the current status and definitions of activity indices in inflammatory bowel disease: how to use indices for precise evaluation. Journal of Gastroenterology. 2022;14:246–266. Gottlieb K, Requa J, Karnes W, Chandra Gudivada R, Shen J, Rael E, et al. Central Reading of Ulcerative Colitis Clinical Trial Videos Using Neural Networks. Gastroenterology. 2021;160(3):710–719.e2. Woo et al. [2023] Woo S, Debnath S, Hu R, Chen X, Liu Z, Kweon IS, et al. ConvNeXt V2: Co-designing and Scaling ConvNets with Masked Autoencoders. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR); 2023. p. 6133–16142. Deng et al. [2009] Deng J, Dong W, Socher R, Li LJ, Li K, Fei-Fei L. Imagenet: A large-scale hierarchical image database. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR); 2009. p. 248–255. Guo et al. [2017] Guo C, Pleiss G, Sun Y, Weinberger KQ. On Calibration of Modern Neural Networks. In: Precup O, Teh YW, editors. Proceedings of the 34th International Conference on Machine Learning. vol. 70 of Proceedings of Machine Learning Research. PMLR; 2017. p. 1321–1330. Kishi et al. [2022] Kishi M, Hirai F, Takatsu N, et al. A review on the current status and definitions of activity indices in inflammatory bowel disease: how to use indices for precise evaluation. Journal of Gastroenterology. 2022;14:246–266. Woo S, Debnath S, Hu R, Chen X, Liu Z, Kweon IS, et al. ConvNeXt V2: Co-designing and Scaling ConvNets with Masked Autoencoders. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR); 2023. p. 6133–16142. Deng et al. [2009] Deng J, Dong W, Socher R, Li LJ, Li K, Fei-Fei L. Imagenet: A large-scale hierarchical image database. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR); 2009. p. 248–255. Guo et al. [2017] Guo C, Pleiss G, Sun Y, Weinberger KQ. On Calibration of Modern Neural Networks. In: Precup O, Teh YW, editors. Proceedings of the 34th International Conference on Machine Learning. vol. 70 of Proceedings of Machine Learning Research. PMLR; 2017. p. 1321–1330. Kishi et al. [2022] Kishi M, Hirai F, Takatsu N, et al. A review on the current status and definitions of activity indices in inflammatory bowel disease: how to use indices for precise evaluation. Journal of Gastroenterology. 2022;14:246–266. Deng J, Dong W, Socher R, Li LJ, Li K, Fei-Fei L. Imagenet: A large-scale hierarchical image database. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR); 2009. p. 248–255. Guo et al. [2017] Guo C, Pleiss G, Sun Y, Weinberger KQ. On Calibration of Modern Neural Networks. In: Precup O, Teh YW, editors. Proceedings of the 34th International Conference on Machine Learning. vol. 70 of Proceedings of Machine Learning Research. PMLR; 2017. p. 1321–1330. Kishi et al. [2022] Kishi M, Hirai F, Takatsu N, et al. A review on the current status and definitions of activity indices in inflammatory bowel disease: how to use indices for precise evaluation. Journal of Gastroenterology. 2022;14:246–266. Guo C, Pleiss G, Sun Y, Weinberger KQ. On Calibration of Modern Neural Networks. In: Precup O, Teh YW, editors. Proceedings of the 34th International Conference on Machine Learning. vol. 70 of Proceedings of Machine Learning Research. PMLR; 2017. p. 1321–1330. Kishi et al. [2022] Kishi M, Hirai F, Takatsu N, et al. A review on the current status and definitions of activity indices in inflammatory bowel disease: how to use indices for precise evaluation. Journal of Gastroenterology. 2022;14:246–266. Kishi M, Hirai F, Takatsu N, et al. A review on the current status and definitions of activity indices in inflammatory bowel disease: how to use indices for precise evaluation. Journal of Gastroenterology. 2022;14:246–266.
- High accuracy in classifying endoscopic severity in ulcerative colitis using convolutional neural network. The American Journal of Gastroenterology. 2022;117(11):1648–1654. Becker et al. [2021] Becker BG, Arcadu F, Thalhammer A, Serna CG, Feehan O, Drawnel F, et al. Training and deploying a deep learning model for endoscopic severity grading in ulcerative colitis using multicenter clinical trial data. Therapeutic Advances in Gastrointestinal Endoscopy. 2021;14:2631774521990623. Lo et al. [2021] Lo B, Liu Z, Bendtsen F, Igel C, Vind I, Burisch TPP Johan. OP07 Artificial intelligence surpasses gastrointestinal experts in the classification of endoscopic severity among Ulcerative Colitis. Journal of Crohn’s and Colitis. 2021 05;15(Supplement):S007–S007. Vaze et al. [2022] Vaze S, Han K, Vedaldi A, Zisserman A. Open-set recognition: A good closed-set classifier is all you need? In: International Conference on Learning Representations (ICLR); 2022. . Gottlieb et al. [2021] Gottlieb K, Requa J, Karnes W, Chandra Gudivada R, Shen J, Rael E, et al. Central Reading of Ulcerative Colitis Clinical Trial Videos Using Neural Networks. Gastroenterology. 2021;160(3):710–719.e2. Woo et al. [2023] Woo S, Debnath S, Hu R, Chen X, Liu Z, Kweon IS, et al. ConvNeXt V2: Co-designing and Scaling ConvNets with Masked Autoencoders. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR); 2023. p. 6133–16142. Deng et al. [2009] Deng J, Dong W, Socher R, Li LJ, Li K, Fei-Fei L. Imagenet: A large-scale hierarchical image database. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR); 2009. p. 248–255. Guo et al. [2017] Guo C, Pleiss G, Sun Y, Weinberger KQ. On Calibration of Modern Neural Networks. In: Precup O, Teh YW, editors. Proceedings of the 34th International Conference on Machine Learning. vol. 70 of Proceedings of Machine Learning Research. PMLR; 2017. p. 1321–1330. Kishi et al. [2022] Kishi M, Hirai F, Takatsu N, et al. A review on the current status and definitions of activity indices in inflammatory bowel disease: how to use indices for precise evaluation. Journal of Gastroenterology. 2022;14:246–266. Becker BG, Arcadu F, Thalhammer A, Serna CG, Feehan O, Drawnel F, et al. Training and deploying a deep learning model for endoscopic severity grading in ulcerative colitis using multicenter clinical trial data. Therapeutic Advances in Gastrointestinal Endoscopy. 2021;14:2631774521990623. Lo et al. [2021] Lo B, Liu Z, Bendtsen F, Igel C, Vind I, Burisch TPP Johan. OP07 Artificial intelligence surpasses gastrointestinal experts in the classification of endoscopic severity among Ulcerative Colitis. Journal of Crohn’s and Colitis. 2021 05;15(Supplement):S007–S007. Vaze et al. [2022] Vaze S, Han K, Vedaldi A, Zisserman A. Open-set recognition: A good closed-set classifier is all you need? In: International Conference on Learning Representations (ICLR); 2022. . Gottlieb et al. [2021] Gottlieb K, Requa J, Karnes W, Chandra Gudivada R, Shen J, Rael E, et al. Central Reading of Ulcerative Colitis Clinical Trial Videos Using Neural Networks. Gastroenterology. 2021;160(3):710–719.e2. Woo et al. [2023] Woo S, Debnath S, Hu R, Chen X, Liu Z, Kweon IS, et al. ConvNeXt V2: Co-designing and Scaling ConvNets with Masked Autoencoders. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR); 2023. p. 6133–16142. Deng et al. [2009] Deng J, Dong W, Socher R, Li LJ, Li K, Fei-Fei L. Imagenet: A large-scale hierarchical image database. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR); 2009. p. 248–255. Guo et al. [2017] Guo C, Pleiss G, Sun Y, Weinberger KQ. On Calibration of Modern Neural Networks. In: Precup O, Teh YW, editors. Proceedings of the 34th International Conference on Machine Learning. vol. 70 of Proceedings of Machine Learning Research. PMLR; 2017. p. 1321–1330. Kishi et al. [2022] Kishi M, Hirai F, Takatsu N, et al. A review on the current status and definitions of activity indices in inflammatory bowel disease: how to use indices for precise evaluation. Journal of Gastroenterology. 2022;14:246–266. Lo B, Liu Z, Bendtsen F, Igel C, Vind I, Burisch TPP Johan. OP07 Artificial intelligence surpasses gastrointestinal experts in the classification of endoscopic severity among Ulcerative Colitis. Journal of Crohn’s and Colitis. 2021 05;15(Supplement):S007–S007. Vaze et al. [2022] Vaze S, Han K, Vedaldi A, Zisserman A. Open-set recognition: A good closed-set classifier is all you need? In: International Conference on Learning Representations (ICLR); 2022. . Gottlieb et al. [2021] Gottlieb K, Requa J, Karnes W, Chandra Gudivada R, Shen J, Rael E, et al. Central Reading of Ulcerative Colitis Clinical Trial Videos Using Neural Networks. Gastroenterology. 2021;160(3):710–719.e2. Woo et al. [2023] Woo S, Debnath S, Hu R, Chen X, Liu Z, Kweon IS, et al. ConvNeXt V2: Co-designing and Scaling ConvNets with Masked Autoencoders. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR); 2023. p. 6133–16142. Deng et al. [2009] Deng J, Dong W, Socher R, Li LJ, Li K, Fei-Fei L. Imagenet: A large-scale hierarchical image database. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR); 2009. p. 248–255. Guo et al. [2017] Guo C, Pleiss G, Sun Y, Weinberger KQ. On Calibration of Modern Neural Networks. In: Precup O, Teh YW, editors. Proceedings of the 34th International Conference on Machine Learning. vol. 70 of Proceedings of Machine Learning Research. PMLR; 2017. p. 1321–1330. Kishi et al. [2022] Kishi M, Hirai F, Takatsu N, et al. A review on the current status and definitions of activity indices in inflammatory bowel disease: how to use indices for precise evaluation. Journal of Gastroenterology. 2022;14:246–266. Vaze S, Han K, Vedaldi A, Zisserman A. Open-set recognition: A good closed-set classifier is all you need? In: International Conference on Learning Representations (ICLR); 2022. . Gottlieb et al. [2021] Gottlieb K, Requa J, Karnes W, Chandra Gudivada R, Shen J, Rael E, et al. Central Reading of Ulcerative Colitis Clinical Trial Videos Using Neural Networks. Gastroenterology. 2021;160(3):710–719.e2. Woo et al. [2023] Woo S, Debnath S, Hu R, Chen X, Liu Z, Kweon IS, et al. ConvNeXt V2: Co-designing and Scaling ConvNets with Masked Autoencoders. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR); 2023. p. 6133–16142. Deng et al. [2009] Deng J, Dong W, Socher R, Li LJ, Li K, Fei-Fei L. Imagenet: A large-scale hierarchical image database. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR); 2009. p. 248–255. Guo et al. [2017] Guo C, Pleiss G, Sun Y, Weinberger KQ. On Calibration of Modern Neural Networks. In: Precup O, Teh YW, editors. Proceedings of the 34th International Conference on Machine Learning. vol. 70 of Proceedings of Machine Learning Research. PMLR; 2017. p. 1321–1330. Kishi et al. [2022] Kishi M, Hirai F, Takatsu N, et al. A review on the current status and definitions of activity indices in inflammatory bowel disease: how to use indices for precise evaluation. Journal of Gastroenterology. 2022;14:246–266. Gottlieb K, Requa J, Karnes W, Chandra Gudivada R, Shen J, Rael E, et al. Central Reading of Ulcerative Colitis Clinical Trial Videos Using Neural Networks. Gastroenterology. 2021;160(3):710–719.e2. Woo et al. [2023] Woo S, Debnath S, Hu R, Chen X, Liu Z, Kweon IS, et al. ConvNeXt V2: Co-designing and Scaling ConvNets with Masked Autoencoders. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR); 2023. p. 6133–16142. Deng et al. [2009] Deng J, Dong W, Socher R, Li LJ, Li K, Fei-Fei L. Imagenet: A large-scale hierarchical image database. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR); 2009. p. 248–255. Guo et al. [2017] Guo C, Pleiss G, Sun Y, Weinberger KQ. On Calibration of Modern Neural Networks. In: Precup O, Teh YW, editors. Proceedings of the 34th International Conference on Machine Learning. vol. 70 of Proceedings of Machine Learning Research. PMLR; 2017. p. 1321–1330. Kishi et al. [2022] Kishi M, Hirai F, Takatsu N, et al. A review on the current status and definitions of activity indices in inflammatory bowel disease: how to use indices for precise evaluation. Journal of Gastroenterology. 2022;14:246–266. Woo S, Debnath S, Hu R, Chen X, Liu Z, Kweon IS, et al. ConvNeXt V2: Co-designing and Scaling ConvNets with Masked Autoencoders. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR); 2023. p. 6133–16142. Deng et al. [2009] Deng J, Dong W, Socher R, Li LJ, Li K, Fei-Fei L. Imagenet: A large-scale hierarchical image database. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR); 2009. p. 248–255. Guo et al. [2017] Guo C, Pleiss G, Sun Y, Weinberger KQ. On Calibration of Modern Neural Networks. In: Precup O, Teh YW, editors. Proceedings of the 34th International Conference on Machine Learning. vol. 70 of Proceedings of Machine Learning Research. PMLR; 2017. p. 1321–1330. Kishi et al. [2022] Kishi M, Hirai F, Takatsu N, et al. A review on the current status and definitions of activity indices in inflammatory bowel disease: how to use indices for precise evaluation. Journal of Gastroenterology. 2022;14:246–266. Deng J, Dong W, Socher R, Li LJ, Li K, Fei-Fei L. Imagenet: A large-scale hierarchical image database. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR); 2009. p. 248–255. Guo et al. [2017] Guo C, Pleiss G, Sun Y, Weinberger KQ. On Calibration of Modern Neural Networks. In: Precup O, Teh YW, editors. Proceedings of the 34th International Conference on Machine Learning. vol. 70 of Proceedings of Machine Learning Research. PMLR; 2017. p. 1321–1330. Kishi et al. [2022] Kishi M, Hirai F, Takatsu N, et al. A review on the current status and definitions of activity indices in inflammatory bowel disease: how to use indices for precise evaluation. Journal of Gastroenterology. 2022;14:246–266. Guo C, Pleiss G, Sun Y, Weinberger KQ. On Calibration of Modern Neural Networks. In: Precup O, Teh YW, editors. Proceedings of the 34th International Conference on Machine Learning. vol. 70 of Proceedings of Machine Learning Research. PMLR; 2017. p. 1321–1330. Kishi et al. [2022] Kishi M, Hirai F, Takatsu N, et al. A review on the current status and definitions of activity indices in inflammatory bowel disease: how to use indices for precise evaluation. Journal of Gastroenterology. 2022;14:246–266. Kishi M, Hirai F, Takatsu N, et al. A review on the current status and definitions of activity indices in inflammatory bowel disease: how to use indices for precise evaluation. Journal of Gastroenterology. 2022;14:246–266.
- Training and deploying a deep learning model for endoscopic severity grading in ulcerative colitis using multicenter clinical trial data. Therapeutic Advances in Gastrointestinal Endoscopy. 2021;14:2631774521990623. Lo et al. [2021] Lo B, Liu Z, Bendtsen F, Igel C, Vind I, Burisch TPP Johan. OP07 Artificial intelligence surpasses gastrointestinal experts in the classification of endoscopic severity among Ulcerative Colitis. Journal of Crohn’s and Colitis. 2021 05;15(Supplement):S007–S007. Vaze et al. [2022] Vaze S, Han K, Vedaldi A, Zisserman A. Open-set recognition: A good closed-set classifier is all you need? In: International Conference on Learning Representations (ICLR); 2022. . Gottlieb et al. [2021] Gottlieb K, Requa J, Karnes W, Chandra Gudivada R, Shen J, Rael E, et al. Central Reading of Ulcerative Colitis Clinical Trial Videos Using Neural Networks. Gastroenterology. 2021;160(3):710–719.e2. Woo et al. [2023] Woo S, Debnath S, Hu R, Chen X, Liu Z, Kweon IS, et al. ConvNeXt V2: Co-designing and Scaling ConvNets with Masked Autoencoders. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR); 2023. p. 6133–16142. Deng et al. [2009] Deng J, Dong W, Socher R, Li LJ, Li K, Fei-Fei L. Imagenet: A large-scale hierarchical image database. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR); 2009. p. 248–255. Guo et al. [2017] Guo C, Pleiss G, Sun Y, Weinberger KQ. On Calibration of Modern Neural Networks. In: Precup O, Teh YW, editors. Proceedings of the 34th International Conference on Machine Learning. vol. 70 of Proceedings of Machine Learning Research. PMLR; 2017. p. 1321–1330. Kishi et al. [2022] Kishi M, Hirai F, Takatsu N, et al. A review on the current status and definitions of activity indices in inflammatory bowel disease: how to use indices for precise evaluation. Journal of Gastroenterology. 2022;14:246–266. Lo B, Liu Z, Bendtsen F, Igel C, Vind I, Burisch TPP Johan. OP07 Artificial intelligence surpasses gastrointestinal experts in the classification of endoscopic severity among Ulcerative Colitis. Journal of Crohn’s and Colitis. 2021 05;15(Supplement):S007–S007. Vaze et al. [2022] Vaze S, Han K, Vedaldi A, Zisserman A. Open-set recognition: A good closed-set classifier is all you need? In: International Conference on Learning Representations (ICLR); 2022. . Gottlieb et al. [2021] Gottlieb K, Requa J, Karnes W, Chandra Gudivada R, Shen J, Rael E, et al. Central Reading of Ulcerative Colitis Clinical Trial Videos Using Neural Networks. Gastroenterology. 2021;160(3):710–719.e2. Woo et al. [2023] Woo S, Debnath S, Hu R, Chen X, Liu Z, Kweon IS, et al. ConvNeXt V2: Co-designing and Scaling ConvNets with Masked Autoencoders. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR); 2023. p. 6133–16142. Deng et al. [2009] Deng J, Dong W, Socher R, Li LJ, Li K, Fei-Fei L. Imagenet: A large-scale hierarchical image database. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR); 2009. p. 248–255. Guo et al. [2017] Guo C, Pleiss G, Sun Y, Weinberger KQ. On Calibration of Modern Neural Networks. In: Precup O, Teh YW, editors. Proceedings of the 34th International Conference on Machine Learning. vol. 70 of Proceedings of Machine Learning Research. PMLR; 2017. p. 1321–1330. Kishi et al. [2022] Kishi M, Hirai F, Takatsu N, et al. A review on the current status and definitions of activity indices in inflammatory bowel disease: how to use indices for precise evaluation. Journal of Gastroenterology. 2022;14:246–266. Vaze S, Han K, Vedaldi A, Zisserman A. Open-set recognition: A good closed-set classifier is all you need? In: International Conference on Learning Representations (ICLR); 2022. . Gottlieb et al. [2021] Gottlieb K, Requa J, Karnes W, Chandra Gudivada R, Shen J, Rael E, et al. Central Reading of Ulcerative Colitis Clinical Trial Videos Using Neural Networks. Gastroenterology. 2021;160(3):710–719.e2. Woo et al. [2023] Woo S, Debnath S, Hu R, Chen X, Liu Z, Kweon IS, et al. ConvNeXt V2: Co-designing and Scaling ConvNets with Masked Autoencoders. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR); 2023. p. 6133–16142. Deng et al. [2009] Deng J, Dong W, Socher R, Li LJ, Li K, Fei-Fei L. Imagenet: A large-scale hierarchical image database. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR); 2009. p. 248–255. Guo et al. [2017] Guo C, Pleiss G, Sun Y, Weinberger KQ. On Calibration of Modern Neural Networks. In: Precup O, Teh YW, editors. Proceedings of the 34th International Conference on Machine Learning. vol. 70 of Proceedings of Machine Learning Research. PMLR; 2017. p. 1321–1330. Kishi et al. [2022] Kishi M, Hirai F, Takatsu N, et al. A review on the current status and definitions of activity indices in inflammatory bowel disease: how to use indices for precise evaluation. Journal of Gastroenterology. 2022;14:246–266. Gottlieb K, Requa J, Karnes W, Chandra Gudivada R, Shen J, Rael E, et al. Central Reading of Ulcerative Colitis Clinical Trial Videos Using Neural Networks. Gastroenterology. 2021;160(3):710–719.e2. Woo et al. [2023] Woo S, Debnath S, Hu R, Chen X, Liu Z, Kweon IS, et al. ConvNeXt V2: Co-designing and Scaling ConvNets with Masked Autoencoders. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR); 2023. p. 6133–16142. Deng et al. [2009] Deng J, Dong W, Socher R, Li LJ, Li K, Fei-Fei L. Imagenet: A large-scale hierarchical image database. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR); 2009. p. 248–255. Guo et al. [2017] Guo C, Pleiss G, Sun Y, Weinberger KQ. On Calibration of Modern Neural Networks. In: Precup O, Teh YW, editors. Proceedings of the 34th International Conference on Machine Learning. vol. 70 of Proceedings of Machine Learning Research. PMLR; 2017. p. 1321–1330. Kishi et al. [2022] Kishi M, Hirai F, Takatsu N, et al. A review on the current status and definitions of activity indices in inflammatory bowel disease: how to use indices for precise evaluation. Journal of Gastroenterology. 2022;14:246–266. Woo S, Debnath S, Hu R, Chen X, Liu Z, Kweon IS, et al. ConvNeXt V2: Co-designing and Scaling ConvNets with Masked Autoencoders. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR); 2023. p. 6133–16142. Deng et al. [2009] Deng J, Dong W, Socher R, Li LJ, Li K, Fei-Fei L. Imagenet: A large-scale hierarchical image database. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR); 2009. p. 248–255. Guo et al. [2017] Guo C, Pleiss G, Sun Y, Weinberger KQ. On Calibration of Modern Neural Networks. In: Precup O, Teh YW, editors. Proceedings of the 34th International Conference on Machine Learning. vol. 70 of Proceedings of Machine Learning Research. PMLR; 2017. p. 1321–1330. Kishi et al. [2022] Kishi M, Hirai F, Takatsu N, et al. A review on the current status and definitions of activity indices in inflammatory bowel disease: how to use indices for precise evaluation. Journal of Gastroenterology. 2022;14:246–266. Deng J, Dong W, Socher R, Li LJ, Li K, Fei-Fei L. Imagenet: A large-scale hierarchical image database. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR); 2009. p. 248–255. Guo et al. [2017] Guo C, Pleiss G, Sun Y, Weinberger KQ. On Calibration of Modern Neural Networks. In: Precup O, Teh YW, editors. Proceedings of the 34th International Conference on Machine Learning. vol. 70 of Proceedings of Machine Learning Research. PMLR; 2017. p. 1321–1330. Kishi et al. [2022] Kishi M, Hirai F, Takatsu N, et al. A review on the current status and definitions of activity indices in inflammatory bowel disease: how to use indices for precise evaluation. Journal of Gastroenterology. 2022;14:246–266. Guo C, Pleiss G, Sun Y, Weinberger KQ. On Calibration of Modern Neural Networks. In: Precup O, Teh YW, editors. Proceedings of the 34th International Conference on Machine Learning. vol. 70 of Proceedings of Machine Learning Research. PMLR; 2017. p. 1321–1330. Kishi et al. [2022] Kishi M, Hirai F, Takatsu N, et al. A review on the current status and definitions of activity indices in inflammatory bowel disease: how to use indices for precise evaluation. Journal of Gastroenterology. 2022;14:246–266. Kishi M, Hirai F, Takatsu N, et al. A review on the current status and definitions of activity indices in inflammatory bowel disease: how to use indices for precise evaluation. Journal of Gastroenterology. 2022;14:246–266.
- OP07 Artificial intelligence surpasses gastrointestinal experts in the classification of endoscopic severity among Ulcerative Colitis. Journal of Crohn’s and Colitis. 2021 05;15(Supplement):S007–S007. Vaze et al. [2022] Vaze S, Han K, Vedaldi A, Zisserman A. Open-set recognition: A good closed-set classifier is all you need? In: International Conference on Learning Representations (ICLR); 2022. . Gottlieb et al. [2021] Gottlieb K, Requa J, Karnes W, Chandra Gudivada R, Shen J, Rael E, et al. Central Reading of Ulcerative Colitis Clinical Trial Videos Using Neural Networks. Gastroenterology. 2021;160(3):710–719.e2. Woo et al. [2023] Woo S, Debnath S, Hu R, Chen X, Liu Z, Kweon IS, et al. ConvNeXt V2: Co-designing and Scaling ConvNets with Masked Autoencoders. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR); 2023. p. 6133–16142. Deng et al. [2009] Deng J, Dong W, Socher R, Li LJ, Li K, Fei-Fei L. Imagenet: A large-scale hierarchical image database. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR); 2009. p. 248–255. Guo et al. [2017] Guo C, Pleiss G, Sun Y, Weinberger KQ. On Calibration of Modern Neural Networks. In: Precup O, Teh YW, editors. Proceedings of the 34th International Conference on Machine Learning. vol. 70 of Proceedings of Machine Learning Research. PMLR; 2017. p. 1321–1330. Kishi et al. [2022] Kishi M, Hirai F, Takatsu N, et al. A review on the current status and definitions of activity indices in inflammatory bowel disease: how to use indices for precise evaluation. Journal of Gastroenterology. 2022;14:246–266. Vaze S, Han K, Vedaldi A, Zisserman A. Open-set recognition: A good closed-set classifier is all you need? In: International Conference on Learning Representations (ICLR); 2022. . Gottlieb et al. [2021] Gottlieb K, Requa J, Karnes W, Chandra Gudivada R, Shen J, Rael E, et al. Central Reading of Ulcerative Colitis Clinical Trial Videos Using Neural Networks. Gastroenterology. 2021;160(3):710–719.e2. Woo et al. [2023] Woo S, Debnath S, Hu R, Chen X, Liu Z, Kweon IS, et al. ConvNeXt V2: Co-designing and Scaling ConvNets with Masked Autoencoders. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR); 2023. p. 6133–16142. Deng et al. [2009] Deng J, Dong W, Socher R, Li LJ, Li K, Fei-Fei L. Imagenet: A large-scale hierarchical image database. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR); 2009. p. 248–255. Guo et al. [2017] Guo C, Pleiss G, Sun Y, Weinberger KQ. On Calibration of Modern Neural Networks. In: Precup O, Teh YW, editors. Proceedings of the 34th International Conference on Machine Learning. vol. 70 of Proceedings of Machine Learning Research. PMLR; 2017. p. 1321–1330. Kishi et al. [2022] Kishi M, Hirai F, Takatsu N, et al. A review on the current status and definitions of activity indices in inflammatory bowel disease: how to use indices for precise evaluation. Journal of Gastroenterology. 2022;14:246–266. Gottlieb K, Requa J, Karnes W, Chandra Gudivada R, Shen J, Rael E, et al. Central Reading of Ulcerative Colitis Clinical Trial Videos Using Neural Networks. Gastroenterology. 2021;160(3):710–719.e2. Woo et al. [2023] Woo S, Debnath S, Hu R, Chen X, Liu Z, Kweon IS, et al. ConvNeXt V2: Co-designing and Scaling ConvNets with Masked Autoencoders. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR); 2023. p. 6133–16142. Deng et al. [2009] Deng J, Dong W, Socher R, Li LJ, Li K, Fei-Fei L. Imagenet: A large-scale hierarchical image database. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR); 2009. p. 248–255. Guo et al. [2017] Guo C, Pleiss G, Sun Y, Weinberger KQ. On Calibration of Modern Neural Networks. In: Precup O, Teh YW, editors. Proceedings of the 34th International Conference on Machine Learning. vol. 70 of Proceedings of Machine Learning Research. PMLR; 2017. p. 1321–1330. Kishi et al. [2022] Kishi M, Hirai F, Takatsu N, et al. A review on the current status and definitions of activity indices in inflammatory bowel disease: how to use indices for precise evaluation. Journal of Gastroenterology. 2022;14:246–266. Woo S, Debnath S, Hu R, Chen X, Liu Z, Kweon IS, et al. ConvNeXt V2: Co-designing and Scaling ConvNets with Masked Autoencoders. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR); 2023. p. 6133–16142. Deng et al. [2009] Deng J, Dong W, Socher R, Li LJ, Li K, Fei-Fei L. Imagenet: A large-scale hierarchical image database. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR); 2009. p. 248–255. Guo et al. [2017] Guo C, Pleiss G, Sun Y, Weinberger KQ. On Calibration of Modern Neural Networks. In: Precup O, Teh YW, editors. Proceedings of the 34th International Conference on Machine Learning. vol. 70 of Proceedings of Machine Learning Research. PMLR; 2017. p. 1321–1330. Kishi et al. [2022] Kishi M, Hirai F, Takatsu N, et al. A review on the current status and definitions of activity indices in inflammatory bowel disease: how to use indices for precise evaluation. Journal of Gastroenterology. 2022;14:246–266. Deng J, Dong W, Socher R, Li LJ, Li K, Fei-Fei L. Imagenet: A large-scale hierarchical image database. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR); 2009. p. 248–255. Guo et al. [2017] Guo C, Pleiss G, Sun Y, Weinberger KQ. On Calibration of Modern Neural Networks. In: Precup O, Teh YW, editors. Proceedings of the 34th International Conference on Machine Learning. vol. 70 of Proceedings of Machine Learning Research. PMLR; 2017. p. 1321–1330. Kishi et al. [2022] Kishi M, Hirai F, Takatsu N, et al. A review on the current status and definitions of activity indices in inflammatory bowel disease: how to use indices for precise evaluation. Journal of Gastroenterology. 2022;14:246–266. Guo C, Pleiss G, Sun Y, Weinberger KQ. On Calibration of Modern Neural Networks. In: Precup O, Teh YW, editors. Proceedings of the 34th International Conference on Machine Learning. vol. 70 of Proceedings of Machine Learning Research. PMLR; 2017. p. 1321–1330. Kishi et al. [2022] Kishi M, Hirai F, Takatsu N, et al. A review on the current status and definitions of activity indices in inflammatory bowel disease: how to use indices for precise evaluation. Journal of Gastroenterology. 2022;14:246–266. Kishi M, Hirai F, Takatsu N, et al. A review on the current status and definitions of activity indices in inflammatory bowel disease: how to use indices for precise evaluation. Journal of Gastroenterology. 2022;14:246–266.
- Open-set recognition: A good closed-set classifier is all you need? In: International Conference on Learning Representations (ICLR); 2022. . Gottlieb et al. [2021] Gottlieb K, Requa J, Karnes W, Chandra Gudivada R, Shen J, Rael E, et al. Central Reading of Ulcerative Colitis Clinical Trial Videos Using Neural Networks. Gastroenterology. 2021;160(3):710–719.e2. Woo et al. [2023] Woo S, Debnath S, Hu R, Chen X, Liu Z, Kweon IS, et al. ConvNeXt V2: Co-designing and Scaling ConvNets with Masked Autoencoders. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR); 2023. p. 6133–16142. Deng et al. [2009] Deng J, Dong W, Socher R, Li LJ, Li K, Fei-Fei L. Imagenet: A large-scale hierarchical image database. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR); 2009. p. 248–255. Guo et al. [2017] Guo C, Pleiss G, Sun Y, Weinberger KQ. On Calibration of Modern Neural Networks. In: Precup O, Teh YW, editors. Proceedings of the 34th International Conference on Machine Learning. vol. 70 of Proceedings of Machine Learning Research. PMLR; 2017. p. 1321–1330. Kishi et al. [2022] Kishi M, Hirai F, Takatsu N, et al. A review on the current status and definitions of activity indices in inflammatory bowel disease: how to use indices for precise evaluation. Journal of Gastroenterology. 2022;14:246–266. Gottlieb K, Requa J, Karnes W, Chandra Gudivada R, Shen J, Rael E, et al. Central Reading of Ulcerative Colitis Clinical Trial Videos Using Neural Networks. Gastroenterology. 2021;160(3):710–719.e2. Woo et al. [2023] Woo S, Debnath S, Hu R, Chen X, Liu Z, Kweon IS, et al. ConvNeXt V2: Co-designing and Scaling ConvNets with Masked Autoencoders. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR); 2023. p. 6133–16142. Deng et al. [2009] Deng J, Dong W, Socher R, Li LJ, Li K, Fei-Fei L. Imagenet: A large-scale hierarchical image database. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR); 2009. p. 248–255. Guo et al. [2017] Guo C, Pleiss G, Sun Y, Weinberger KQ. On Calibration of Modern Neural Networks. In: Precup O, Teh YW, editors. Proceedings of the 34th International Conference on Machine Learning. vol. 70 of Proceedings of Machine Learning Research. PMLR; 2017. p. 1321–1330. Kishi et al. [2022] Kishi M, Hirai F, Takatsu N, et al. A review on the current status and definitions of activity indices in inflammatory bowel disease: how to use indices for precise evaluation. Journal of Gastroenterology. 2022;14:246–266. Woo S, Debnath S, Hu R, Chen X, Liu Z, Kweon IS, et al. ConvNeXt V2: Co-designing and Scaling ConvNets with Masked Autoencoders. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR); 2023. p. 6133–16142. Deng et al. [2009] Deng J, Dong W, Socher R, Li LJ, Li K, Fei-Fei L. Imagenet: A large-scale hierarchical image database. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR); 2009. p. 248–255. Guo et al. [2017] Guo C, Pleiss G, Sun Y, Weinberger KQ. On Calibration of Modern Neural Networks. In: Precup O, Teh YW, editors. Proceedings of the 34th International Conference on Machine Learning. vol. 70 of Proceedings of Machine Learning Research. PMLR; 2017. p. 1321–1330. Kishi et al. [2022] Kishi M, Hirai F, Takatsu N, et al. A review on the current status and definitions of activity indices in inflammatory bowel disease: how to use indices for precise evaluation. Journal of Gastroenterology. 2022;14:246–266. Deng J, Dong W, Socher R, Li LJ, Li K, Fei-Fei L. Imagenet: A large-scale hierarchical image database. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR); 2009. p. 248–255. Guo et al. [2017] Guo C, Pleiss G, Sun Y, Weinberger KQ. On Calibration of Modern Neural Networks. In: Precup O, Teh YW, editors. Proceedings of the 34th International Conference on Machine Learning. vol. 70 of Proceedings of Machine Learning Research. PMLR; 2017. p. 1321–1330. Kishi et al. [2022] Kishi M, Hirai F, Takatsu N, et al. A review on the current status and definitions of activity indices in inflammatory bowel disease: how to use indices for precise evaluation. Journal of Gastroenterology. 2022;14:246–266. Guo C, Pleiss G, Sun Y, Weinberger KQ. On Calibration of Modern Neural Networks. In: Precup O, Teh YW, editors. Proceedings of the 34th International Conference on Machine Learning. vol. 70 of Proceedings of Machine Learning Research. PMLR; 2017. p. 1321–1330. Kishi et al. [2022] Kishi M, Hirai F, Takatsu N, et al. A review on the current status and definitions of activity indices in inflammatory bowel disease: how to use indices for precise evaluation. Journal of Gastroenterology. 2022;14:246–266. Kishi M, Hirai F, Takatsu N, et al. A review on the current status and definitions of activity indices in inflammatory bowel disease: how to use indices for precise evaluation. Journal of Gastroenterology. 2022;14:246–266.
- Central Reading of Ulcerative Colitis Clinical Trial Videos Using Neural Networks. Gastroenterology. 2021;160(3):710–719.e2. Woo et al. [2023] Woo S, Debnath S, Hu R, Chen X, Liu Z, Kweon IS, et al. ConvNeXt V2: Co-designing and Scaling ConvNets with Masked Autoencoders. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR); 2023. p. 6133–16142. Deng et al. [2009] Deng J, Dong W, Socher R, Li LJ, Li K, Fei-Fei L. Imagenet: A large-scale hierarchical image database. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR); 2009. p. 248–255. Guo et al. [2017] Guo C, Pleiss G, Sun Y, Weinberger KQ. On Calibration of Modern Neural Networks. In: Precup O, Teh YW, editors. Proceedings of the 34th International Conference on Machine Learning. vol. 70 of Proceedings of Machine Learning Research. PMLR; 2017. p. 1321–1330. Kishi et al. [2022] Kishi M, Hirai F, Takatsu N, et al. A review on the current status and definitions of activity indices in inflammatory bowel disease: how to use indices for precise evaluation. Journal of Gastroenterology. 2022;14:246–266. Woo S, Debnath S, Hu R, Chen X, Liu Z, Kweon IS, et al. ConvNeXt V2: Co-designing and Scaling ConvNets with Masked Autoencoders. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR); 2023. p. 6133–16142. Deng et al. [2009] Deng J, Dong W, Socher R, Li LJ, Li K, Fei-Fei L. Imagenet: A large-scale hierarchical image database. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR); 2009. p. 248–255. Guo et al. [2017] Guo C, Pleiss G, Sun Y, Weinberger KQ. On Calibration of Modern Neural Networks. In: Precup O, Teh YW, editors. Proceedings of the 34th International Conference on Machine Learning. vol. 70 of Proceedings of Machine Learning Research. PMLR; 2017. p. 1321–1330. Kishi et al. [2022] Kishi M, Hirai F, Takatsu N, et al. A review on the current status and definitions of activity indices in inflammatory bowel disease: how to use indices for precise evaluation. Journal of Gastroenterology. 2022;14:246–266. Deng J, Dong W, Socher R, Li LJ, Li K, Fei-Fei L. Imagenet: A large-scale hierarchical image database. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR); 2009. p. 248–255. Guo et al. [2017] Guo C, Pleiss G, Sun Y, Weinberger KQ. On Calibration of Modern Neural Networks. In: Precup O, Teh YW, editors. Proceedings of the 34th International Conference on Machine Learning. vol. 70 of Proceedings of Machine Learning Research. PMLR; 2017. p. 1321–1330. Kishi et al. [2022] Kishi M, Hirai F, Takatsu N, et al. A review on the current status and definitions of activity indices in inflammatory bowel disease: how to use indices for precise evaluation. Journal of Gastroenterology. 2022;14:246–266. Guo C, Pleiss G, Sun Y, Weinberger KQ. On Calibration of Modern Neural Networks. In: Precup O, Teh YW, editors. Proceedings of the 34th International Conference on Machine Learning. vol. 70 of Proceedings of Machine Learning Research. PMLR; 2017. p. 1321–1330. Kishi et al. [2022] Kishi M, Hirai F, Takatsu N, et al. A review on the current status and definitions of activity indices in inflammatory bowel disease: how to use indices for precise evaluation. Journal of Gastroenterology. 2022;14:246–266. Kishi M, Hirai F, Takatsu N, et al. A review on the current status and definitions of activity indices in inflammatory bowel disease: how to use indices for precise evaluation. Journal of Gastroenterology. 2022;14:246–266.
- ConvNeXt V2: Co-designing and Scaling ConvNets with Masked Autoencoders. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR); 2023. p. 6133–16142. Deng et al. [2009] Deng J, Dong W, Socher R, Li LJ, Li K, Fei-Fei L. Imagenet: A large-scale hierarchical image database. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR); 2009. p. 248–255. Guo et al. [2017] Guo C, Pleiss G, Sun Y, Weinberger KQ. On Calibration of Modern Neural Networks. In: Precup O, Teh YW, editors. Proceedings of the 34th International Conference on Machine Learning. vol. 70 of Proceedings of Machine Learning Research. PMLR; 2017. p. 1321–1330. Kishi et al. [2022] Kishi M, Hirai F, Takatsu N, et al. A review on the current status and definitions of activity indices in inflammatory bowel disease: how to use indices for precise evaluation. Journal of Gastroenterology. 2022;14:246–266. Deng J, Dong W, Socher R, Li LJ, Li K, Fei-Fei L. Imagenet: A large-scale hierarchical image database. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR); 2009. p. 248–255. Guo et al. [2017] Guo C, Pleiss G, Sun Y, Weinberger KQ. On Calibration of Modern Neural Networks. In: Precup O, Teh YW, editors. Proceedings of the 34th International Conference on Machine Learning. vol. 70 of Proceedings of Machine Learning Research. PMLR; 2017. p. 1321–1330. Kishi et al. [2022] Kishi M, Hirai F, Takatsu N, et al. A review on the current status and definitions of activity indices in inflammatory bowel disease: how to use indices for precise evaluation. Journal of Gastroenterology. 2022;14:246–266. Guo C, Pleiss G, Sun Y, Weinberger KQ. On Calibration of Modern Neural Networks. In: Precup O, Teh YW, editors. Proceedings of the 34th International Conference on Machine Learning. vol. 70 of Proceedings of Machine Learning Research. PMLR; 2017. p. 1321–1330. Kishi et al. [2022] Kishi M, Hirai F, Takatsu N, et al. A review on the current status and definitions of activity indices in inflammatory bowel disease: how to use indices for precise evaluation. Journal of Gastroenterology. 2022;14:246–266. Kishi M, Hirai F, Takatsu N, et al. A review on the current status and definitions of activity indices in inflammatory bowel disease: how to use indices for precise evaluation. Journal of Gastroenterology. 2022;14:246–266.
- Imagenet: A large-scale hierarchical image database. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR); 2009. p. 248–255. Guo et al. [2017] Guo C, Pleiss G, Sun Y, Weinberger KQ. On Calibration of Modern Neural Networks. In: Precup O, Teh YW, editors. Proceedings of the 34th International Conference on Machine Learning. vol. 70 of Proceedings of Machine Learning Research. PMLR; 2017. p. 1321–1330. Kishi et al. [2022] Kishi M, Hirai F, Takatsu N, et al. A review on the current status and definitions of activity indices in inflammatory bowel disease: how to use indices for precise evaluation. Journal of Gastroenterology. 2022;14:246–266. Guo C, Pleiss G, Sun Y, Weinberger KQ. On Calibration of Modern Neural Networks. In: Precup O, Teh YW, editors. Proceedings of the 34th International Conference on Machine Learning. vol. 70 of Proceedings of Machine Learning Research. PMLR; 2017. p. 1321–1330. Kishi et al. [2022] Kishi M, Hirai F, Takatsu N, et al. A review on the current status and definitions of activity indices in inflammatory bowel disease: how to use indices for precise evaluation. Journal of Gastroenterology. 2022;14:246–266. Kishi M, Hirai F, Takatsu N, et al. A review on the current status and definitions of activity indices in inflammatory bowel disease: how to use indices for precise evaluation. Journal of Gastroenterology. 2022;14:246–266.
- On Calibration of Modern Neural Networks. In: Precup O, Teh YW, editors. Proceedings of the 34th International Conference on Machine Learning. vol. 70 of Proceedings of Machine Learning Research. PMLR; 2017. p. 1321–1330. Kishi et al. [2022] Kishi M, Hirai F, Takatsu N, et al. A review on the current status and definitions of activity indices in inflammatory bowel disease: how to use indices for precise evaluation. Journal of Gastroenterology. 2022;14:246–266. Kishi M, Hirai F, Takatsu N, et al. A review on the current status and definitions of activity indices in inflammatory bowel disease: how to use indices for precise evaluation. Journal of Gastroenterology. 2022;14:246–266.
- A review on the current status and definitions of activity indices in inflammatory bowel disease: how to use indices for precise evaluation. Journal of Gastroenterology. 2022;14:246–266.