Computer Vision-Based Retrieval of Steps and Errors in Laparoscopic Cholecystectomy

Traditional surgical training relies heavily on hands-on experiences gained through relatively infrequent procedures during apprenticeships. Recently, postoperative review has become a valuable supplement to this model, offering learning opportunities outside the operating room. However, its adoption remains limited due to its inefficiencies. In this study, we developed a Computer Vision-based system designed to efficiently navigate and retrieve critical segments from laparoscopic cholecystectomy videos. Trained on a manually annotated dataset of 683 videos from six different sources, our model is taught to identify five distinct surgical steps and three common intraoperative errors: bleeding, bile spillage, and thermal injury. The resulting models demonstrated strong generalizability in identifying steps to unseen data from new hospitals. Large-scale analysis further revealed correlations between model-predicted error events and surgical step, disease severity, and surgical skill that align with established clinical knowledge. By facilitating the rapid retrieval of clinically relevant events, this tool enhances postoperative review practical, ultimately expediting surgical training.