DANIEL FREEDMAN

Background The potential role and benefits of AI in surgery has yet to be determined. This study is a first step in developing
an AI system for minimizing adverse events and improving patient’s safety. We developed an Artificial Intelligence (AI)
algorithm and evaluated its performance in recognizing surgical phases of laparoscopic cholecystectomy (LC) videos spanning a range of complexities.Methods A set of 371 LC videos with various complexity levels and containing adverse events was collected from five
hospitals. Two expert surgeons segmented each video into 10 phases including Calot’s triangle dissection and clipping and
cutting. For each video, adverse events were also annotated when present (major bleeding; gallbladder perforation; major
bile leakage; and incidental finding) and complexity level (on a scale of 1–5) was also recorded. The dataset was then split
in an 80:20 ratio (294 and 77 videos), stratified by complexity, hospital, and adverse events to train and test the AI model,
respectively. The AI-surgeon agreement was then compared to the agreement between surgeons.Results The mean accuracy of the AI model for surgical phase recognition was 89% [95% CI 87.1%, 90.6%], comparable
to the mean inter-annotator agreement of 90% [95% CI 89.4%, 90.5%]. The model’s accuracy was inversely associated with
procedure complexity, decreasing from 92% (complexity level 1) to 88% (complexity level 3) to 81% (complexity level 5).Conclusion The AI model successfully identified surgical phases in both simple and complex LC procedures. Further validation and system training is warranted to evaluate its potential applications such as to increase patient safety during surgery.