Artificial intelligence-based automated laparoscopic cholecystectomy surgical phase recognition and analysis

• Published in: Surgical endoscopy Vol. 36; no. 5; pp. 3160 - 3168
• Main Authors: Cheng, Ke, You, Jiaying, Wu, Shangdi, Chen, Zixin, Zhou, Zijian, Guan, Jingye, Peng, Bing, Wang, Xin
• Format: Journal Article
• Language: English
• Published: New York Springer US 01.05.2022; Springer Nature B.V
• Subjects: Abdominal Surgery; Accuracy; Artificial Intelligence; Cholecystectomy; Cholecystectomy, Laparoscopic; Computer vision; Gastroenterology; Gynecology; Hepatology; Humans; Laparoscopy; Medicine; Medicine & Public Health; Proctology; Surgeons; Surgery; Laparoscopic cholecystectomy; Artificial intelligence; Surgical phase recognition
• ISSN: 0930-2794; 1432-2218
• DOI: 10.1007/s00464-021-08619-3

Background Artificial intelligence and computer vision have revolutionized laparoscopic surgical video analysis. However, there is no multi-center study focused on deep learning-based laparoscopic cholecystectomy phases recognizing. This work aims to apply artificial intelligence in recognizing and analyzing phases in laparoscopic cholecystectomy videos from multiple centers. Methods This observational cohort-study included 163 laparoscopic cholecystectomy videos collected from four medical centers. Videos were labeled by surgeons and a deep-learning model was developed based on 90 videos. Thereafter, the performance of the model was tested in additional ten videos by comparing it with the annotated ground truth of the surgeon. Deep-learning models were trained to identify laparoscopic cholecystectomy phases. The performance of models was measured using precision, recall, F1 score, and overall accuracy. With a high overall accuracy of the model, additional 63 videos as an analysis set were analyzed by the model to identify different phases. Results Mean concordance correlation coefficient for annotations of the surgeons across all operative phases was 92.38%. Also, the overall phase recognition accuracy of laparoscopic cholecystectomy by the model was 91.05%. In the analysis set, there was an average surgery time of 2195 ± 896 s, with a huge individual variance of different surgical phases. Notably, laparoscopic cholecystectomy in acute cholecystitis cases had prolonged overall durations, and the surgeon would spend more time in mobilizing the hepatocystic triangle phase. Conclusion A deep-learning model based on multiple centers data can identify phases of laparoscopic cholecystectomy with a high degree of accuracy. With continued refinements, artificial intelligence could be utilized in huge data surgery analysis to achieve clinically relevant future applications.