Automated models enhance competency assessment in laparoscopic surgery

The study demonstrates the development of automated surgical action recognition models, achieving significant accuracy in competency prediction during laparoscopic cholecystectomy. Analysis of the cholec80 dataset revealed that high-competency groups exhibited shorter dissection durations and higher scores on established evaluation metrics. A random forest model achieved 93% accuracy in predicting surgical competency, while a video-masked autoencoder reached 89.11% accuracy in recognizing surgical actions. These advancements could transform surgical education by providing precise performance feedback.