• Pietro Mascagni, Deepak Alapatt, Takeshi Urade, Armine Vardazaryan, Didier Mutter, Jacques Marescaux, Guido Costamagna, Bernard Dallemagne, and Nicolas Padoy.
• ICube, University of Strasbourg, CNRS, IHU Strasbourg, France.
• Ann. Surg. 2021 Jul 1; 274 (1): e93e95e93-e95.

ObjectiveThe aim of this study was to develop a computer vision platform to automatically locate critical events in surgical videos and provide short video clips documenting the critical view of safety (CVS) in laparoscopic cholecystectomy (LC).BackgroundIntraoperative events are typically documented through operator-dictated reports that do not always translate the operative reality. Surgical videos provide complete information on surgical procedures, but the burden associated with storing and manually analyzing full-length videos has so far limited their effective use.MethodsA computer vision platform named EndoDigest was developed and used to analyze LC videos. The mean absolute error (MAE) of the platform in automatically locating the manually annotated time of the cystic duct division in full-length videos was assessed. The relevance of the automatically extracted short video clips was evaluated by calculating the percentage of video clips in which the CVS was assessable by surgeons.ResultsA total of 155 LC videos were analyzed: 55 of these videos were used to develop EndoDigest, whereas the remaining 100 were used to test it. The time of the cystic duct division was automatically located with a MAE of 62.8 ± 130.4 seconds (1.95% of full-length video duration). CVS was assessable in 91% of the 2.5 minutes long video clips automatically extracted from the considered test procedures.ConclusionsDeep learning models for workflow analysis can be used to reliably locate critical events in surgical videos and document CVS in LC. Further studies are needed to assess the clinical impact of surgical data science solutions for safer laparoscopic cholecystectomy.Copyright © 2021 Wolters Kluwer Health, Inc. All rights reserved.

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