Surgical Task

Surgical task automation and assessment are active research areas aiming to improve surgical training and the efficiency and safety of procedures. Current efforts focus on developing robust machine learning models, including reinforcement learning, imitation learning, and various neural network architectures (e.g., convolutional, recurrent, Siamese networks), to automate tasks, provide objective skill assessments from video data, and detect errors in real-time. These advancements leverage multimodal data (video, kinematic data, physiological signals) and aim to create more efficient training methods and improve the overall quality and safety of surgical interventions. The ultimate goal is to enhance surgical outcomes through improved training, automation, and real-time error detection.