Surgical Skill Assessment

Surgical skill assessment aims to objectively evaluate surgeon proficiency, improving patient safety and surgical training. Current research heavily utilizes machine learning, employing various architectures like convolutional neural networks (CNNs), recurrent neural networks (RNNs), transformers, and graph neural networks (GNNs) to analyze video and kinematic data from both simulated and real surgical procedures. These models often focus on instrument tracking, action segmentation, and the extraction of meaningful motion features to predict skill levels and provide real-time feedback. This automated assessment offers a significant advancement over traditional subjective methods, potentially leading to more efficient training, improved surgical outcomes, and enhanced quality assurance.