Automatized Self-Supervised Learning for Skin Lesion Screening

The incidence rates of melanoma, the deadliest form of skin cancer, have been increasing steadily worldwide, presenting a significant challenge to dermatologists. Early detection of melanoma is crucial for improving patient survival rates, but identifying suspicious lesions through ugly duckling (UD) screening, the current method used for skin cancer screening, can be challenging and often requires expertise in pigmented lesions. To address these challenges and improve patient outcomes, an AI decision support tool was developed to assist dermatologists in identifying UD from wide-field patient images. The tool uses a state-of-the-art object detection algorithm to identify and extract all skin lesions from patient images, which are then sorted by suspiciousness using a self-supervised AI algorithm. A clinical validation study was conducted to evaluate the tool's performance, which demonstrated an average sensitivity of 93% for the top-10 AI-identified UDs on skin lesions selected by the majority of experts in pigmented skin lesions. The study also found that dermatologists confidence increased, and the average majority agreement with the top-10 AI-identified UDs improved to 100% when assisted by AI. The development of this AI decision support tool aims to address the shortage of specialists, enable at-risk patients to receive faster consultations and understand the impact of AI-assisted screening. The tool's automation can assist dermatologists in identifying suspicious lesions and provide a more objective assessment, reducing subjectivity in the screening process. The future steps for this project include expanding the dataset to include histologically confirmed melanoma cases and increasing the number of participants for clinical validation to strengthen the tool's reliability and adapt it for real-world consultation.