Situating Artificial Intelligence in Surgery: A Focus on Disease Severity. Issue 3 (September 2020)

• Record Type: Journal Article
• Authors: Korndorffer, James R.; Hawn, Mary T.; Spain, David A.; Knowlton, Lisa M.; Azagury, Dan E.; Nassar, Aussama K.; Lau, James N.; Arnow, Katherine D.; Trickey, Amber W.; Pugh, Carla M.
• Is Part Of: Annals of surgery. Volume 272:Issue 3(2020)
• Publication Date: 2020-09
• Subjects: artificial intelligence -- cholecystectomy -- critical view of safety -- quality assurance -- video-based assessment; Surgery -- Periodicals; 617.005
• DOI: 10.1097/SLA.0000000000004207
• Languages: English
• ISSNs: 0003-4932

Abstract

Abstract : Objectives: Artificial intelligence (AI) has numerous applications in surgical quality assurance. We assessed AI accuracy in evaluating the critical view of safety (CVS) and intraoperative events during laparoscopic cholecystectomy. We hypothesized that AI accuracy and intraoperative events are associated with disease severity. Methods: One thousand fifty-one laparoscopic cholecystectomy videos were annotated by AI for disease severity (Parkland Scale), CVS achievement (Strasberg Criteria), and intraoperative events. Surgeons performed focused video review on procedures with ≥1 intraoperative events (n = 335). AI versus surgeon annotation of CVS components and intraoperative events were compared. For all cases (n = 1051), intraoperative-event association with CVS achievement and severity was examined using ordinal logistic regression. Results: Using AI annotation, surgeons reviewed 50 videos/hr. CVS was achieved in ⩽10% of cases. Hepatocystic triangle and cystic plate visualization was achieved more often in low-severity cases ( P < 0.03). AI-surgeon agreement for all CVS components exceeded 75%, with higher agreement in high-severity cases ( P < 0.03). Surgeons agreed with 99% of AI-annotated intraoperative events. AI-annotated intraoperative events were associated with both disease severity and number of CVS components not achieved. Intraoperative events occurred more frequently in high-severity versus low-severity cases (0.98 vs 0.40 events/case, P < 0.001). Conclusions: AI annotation allows for efficient video review and is a promising quality assurance tool. Disease severity may limit its use and surgeon oversight is still required, especially in complex cases. Continued refinement may improve AI applicability and allow for automated assessment.