Toward a Surgeon-in-the-Loop Ophthalmic Robotic Apprentice using Reinforcement and Imitation Learning
(2311.17693)Abstract
Robotic-assisted surgical systems have demonstrated significant potential in enhancing surgical precision and minimizing human errors. However, existing systems lack the ability to accommodate the unique preferences and requirements of individual surgeons. Additionally, they primarily focus on general surgeries (e.g., laparoscopy) and are not suitable for highly precise microsurgeries, such as ophthalmic procedures. Thus, we propose a simulation-based image-guided approach for surgeon-centered autonomous agents that can adapt to the individual surgeon's skill level and preferred surgical techniques during ophthalmic cataract surgery. Our approach utilizes a simulated environment to train reinforcement and imitation learning agents guided by image data to perform all tasks of the incision phase of cataract surgery. By integrating the surgeon's actions and preferences into the training process with the surgeon-in-the-loop, our approach enables the robot to implicitly learn and adapt to the individual surgeon's unique approach through demonstrations. This results in a more intuitive and personalized surgical experience for the surgeon. Simultaneously, it ensures consistent performance for the autonomous robotic apprentice. We define and evaluate the effectiveness of our approach using our proposed metrics; and highlight the trade-off between a generic agent and a surgeon-centered adapted agent. Moreover, our approach has the potential to extend to other ophthalmic surgical procedures, opening the door to a new generation of surgeon-in-the-loop autonomous surgical robots. We provide an open-source simulation framework for future development and reproducibility.
Overview
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A new approach for training robotic assistants in ophthalmic surgery through a simulation-based platform using reinforcement and imitation learning.
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A 3D simulated model of the human eye used in tandem with virtual tools to teach agents surgical techniques.
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Introduction of new performance metrics like the Surgery Completion Rate and Adaptive Surgery Success Rate to evaluate the agents.
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Results showed a trade-off between adaptation to a surgeon's style and surgical completion, with an increase in agents' ability to emulate complex techniques.
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The research provides a foundation for physically integrable, customizable robotic surgical apprentices that could advance personalized healthcare.
Introduction
Robotic-assisted surgery has become an indispensable part of modern medicine, offering increased precision and reduced human error. Yet, these systems often fall short when it comes to adjusting to the unique styles and preferences of individual surgeons. This gap is particularly notable in the field of ophthalmology, where surgeons perform delicate cataract surgeries requiring high precision. Addressing this, a novel simulation-based approach has been introduced to train autonomous agents for surgeon-centered assistance in ophthalmic procedures.
Simulation and Training
The core of the approach lies in a 3D simulated model of the human eye matched with a set of virtual microsurgery tools. This platform serves as a training ground to teach agents through reinforcement and imitation learning. The process begins with the agent learning basic surgical maneuvers, progressing gradually from a less complex (low-poly) environment to a feature-rich (high-poly) simulation. The system cleverly integrates the surgeon's expertise by learning from demonstrations, allowing the agent to observe and adapt to specific surgical techniques.
Metrics for Success
To gauge the success and adaptability of the trained agents, new performance metrics have been introduced. The Surgery Completion Rate (SCR) measures the agent's ability to perform surgery without causing unintended damage, whereas the Adaptive Surgery Success Rate (AdSSR) assesses how well the agent can emulate the specific approach shown in the expert demonstrations. These metrics provide an objective lens to scrutinize and validate the efficacy of the agents.
Results and Implications
The findings demonstrate that while higher adaptation to a surgeon's technique may decrease SCR due to increased complexity, there is a pronounced improvement in the AdSSR. This improvement indicates that the agents are effectively learning the nuanced techniques shown in the expert demonstrations. Notably, the study's methodology furnishes the groundwork for integrating these learned behaviors into physical robots in the future, potentially revolutionizing the field by offering a level of personalized robotic assistance that adapts in real-time to the surgeons' varying techniques.
Conclusion
This research opens a promising path toward customizable robotic apprentices for surgery, which can adapt to a surgeon’s preferences and skill level. The proposed framework paves the way for robots that learn from demonstrations to exhibit an unprecedented level of precision and adaptability in surgery. The open-source nature of the simulation environment further facilitates progress in this field by allowing for collaborative developments and enhancements.
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