Real-time artificial intelligence validation of critical view of safety in laparoscopic cholecystectomy

Abstract

Background

Critical View of Safety (CVS) is the accepted strategy to avoid bile duct injury during Laparoscopic Cholecystectomy (LC). In this study, we sought to investigate the accuracy and performance of a trained Artificial Intelligent (AI) model in validation of the CVS achievement during elective LC in a real time operating room setting.

Study design

A deep learning neural network which was previously trained on annotated segments of 700 LC videos to identify the CVS criteria, was integrated into the operating room laparoscopic video system, for continuous monitoring and real-time validation of CVS achievement during elective LC procedures. The system's feedback and surgeon's report were recorded and compared, as well as the overall rate of CVS achievement.

Results

Of 40 consecutive LC, CVS was reported by the surgeons in 34 (85 %). In all the 6 cases where CVS was not achieved due to severe inflammation or anatomy distortion, the AI model agreed with surgeon's report and did not identify CVS. Out of the 34 cases where CVS was achieved, the AI model identified 33. Thus, the AI model detected the CVS achievement with a specificity of 100 % [95%-CI 98.1 %, 100 %] and sensitivity of 97 % [95%-CI 96.1 %, 98.2 %].

Conclusions

A trained AI model can identify CVS during elective LC with very high accuracy in a real time OR setting. Additionally, its use may result in high rates of CVS achievement, thereby improving LC procedure's safety and outcome.