AI-driven precision: Reinforcement learning-based control for microscope-assisted robotic retina surgery

Microscope-assisted robotic retina surgery has emerged as a transformative approach for improving precision in delicate retinal procedures. Traditional manual techniques rely heavily on surgeons’ dexterity, leading to variability in surgical outcomes and increased risks of human errors. Existing robotic systems improve stability but lack adaptability to dynamic surgical environments. This study introduced a reinforcement learning-based control mechanism for robotic arm adjustments in retinal surgeries, addressing the need for an intelligent, real-time responsive system. The proposed study explored three methodologies, namely, Reinforcement Learning (RL), Deep Deterministic Policy Gradient (DDPG), and Generative Adversarial Imitation Learning (GAIL). The RL model utilized a Markov Decision Process framework to iteratively refine robotic movements based on reward feedback. The DDPG approach integrated an actor-critic model, enabling continuous learning for precise robotic control. The GAIL model, trained on expert demonstrations, leveraged adversarial learning to ensure that the robotic arm mimicked surgeon-like movements. A simulation environment in V-REP was used to validate the suggested models, and performance was evaluated using F1-score, accuracy, precision, recall, and reward-based learning efficiency. The accuracy of the RL model was 95%, but the DDPG model outperformed it to 98%. GAIL, on the other hand, showed excellent results with an area under the curve of 0.99% and 99% accuracy, indicating almost flawless classification of the best surgical actions. The GAIL approach exhibited minimal false positives, enhanced precision, and improved trajectory prediction for retinal surgeries, making it the most effective model.