Automated surgical action recognition and competency assessment in laparoscopic cholecystectomy: a proof-of-concept study.

BACKGROUND

Laparoscopic cholecystectomy (LC) is a common procedure with standardized steps and validated assessment tools. However, the role of surgical actions in competency assessment remains underexplored, and automated models for surgical action recognition are lacking.

METHODS

The Cholec80 dataset of 80 LC videos was analyzed for the Calot's Triangle Dissection (CTD) phase. Strasberg's critical view of safety (CVS) score and second-by-second annotations of surgical actions were evaluated. Videos were categorized into high_simple, low_simple, and high_complex groups based on competency levels and cholecystitis grade. The dataset was randomly divided into training (66 videos) and testing (14 videos) sets based on subgrouping. Surgical metrics were compared between subgroups, and a Random Forest model was constructed to predict competency levels using these metrics. In addition, a Video-Masked Autoencoders (VideoMAE) model was developed for surgical action recognition.

RESULTS

The high_simple group had significantly shorter CTD duration, fewer action transitions, and lower percentages of suctioning/irrigating, coagulating, and idle actions, but higher CVS scores and dissecting percentages. The Random Forest model achieved 93% accuracy (AUC: 0.96) in competency prediction, with CVS score, CTD duration, and percentages of dissecting, coagulating, and exposing as the top five important features. The VideoMAE model attained 89.11% overall accuracy in recognizing surgical actions, with the highest recall (0.97) for dissecting and the lowest (0.51) for suctioning/irrigating.

CONCLUSIONS

This study highlights the importance of surgical actions in competency assessment and presents automated models for evaluation and action recognition. These tools have potential to transform surgical education by providing objective and data-driven feedback for skill improvement.