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.
Journal Article by Yen HH, Hsiao YH (…) Ho MC et 5 al. in Surg Endosc
© 2025. The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.