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Situating Artificial Intelligence In Surgery: A Focus On Disease Severity
James R Korndorffer, Jr., Mary T Hawn, David A Spain, *Lisa M Knowlton, *Dan E Azagury, *Aussama K Nassar, *James N Lau, *Katherine Arnow, *Amber Trickey, Carla M Pugh
Stanford University, Stanford, CA

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 and hypothesized that AI accuracy and intraoperative-events are associated with disease severity.
METHODS: 1051 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=225). AI vs. surgeon annotation of CVS components and intraoperative-events was compared. For all cases (n=1051), intraoperative-event association with CVS achievement and severity was examined using ordinal logistic regression.
RESULTS: With AI, 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% (kappa 0.19-0.54), with higher agreement in low-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 (Table). Intraoperative-events occurred more frequently in high-severity vs. 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 in complex cases. Continued refinement may improve AI applicability and allow for automated assessment.


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