Multicentric validation of EndoDigest: a computer vision platform for video documentation of the critical view of safety in laparoscopic cholecystectomy.

Autor: Mascagni P; ICube, University of Strasbourg, CNRS, c/o IHU-Strasbourg, 1, place de l'hôpital, 67000, Strasbourg, France. p.mascagni@unistra.fr.; Fondazione Policlinico Universitario A. Gemelli IRCCS, Rome, Italy. p.mascagni@unistra.fr., Alapatt D; ICube, University of Strasbourg, CNRS, c/o IHU-Strasbourg, 1, place de l'hôpital, 67000, Strasbourg, France., Laracca GG; Department of Medical Surgical Science and Translational Medicine, Sant'Andrea Hospital, Sapienza University of Rome, Rome, Italy., Guerriero L; Department of Laparoscopic and Robotic General Surgery, Monaldi Hospital, AORN dei Colli, Naples, Italy., Spota A; Scuola di Specializzazione in Chirurgia Generale, University of Milan, Milan, Italy., Fiorillo C; Fondazione Policlinico Universitario A. Gemelli IRCCS, Rome, Italy., Vardazaryan A; ICube, University of Strasbourg, CNRS, c/o IHU-Strasbourg, 1, place de l'hôpital, 67000, Strasbourg, France., Quero G; Fondazione Policlinico Universitario A. Gemelli IRCCS, Rome, Italy., Alfieri S; Fondazione Policlinico Universitario A. Gemelli IRCCS, Rome, Italy., Baldari L; Department of Surgery, Fondazione IRCCS Ca' Granda Ospedale Maggiore Policlinico di Milano, University of Milan, Milan, Italy., Cassinotti E; Department of Surgery, Fondazione IRCCS Ca' Granda Ospedale Maggiore Policlinico di Milano, University of Milan, Milan, Italy., Boni L; Department of Surgery, Fondazione IRCCS Ca' Granda Ospedale Maggiore Policlinico di Milano, University of Milan, Milan, Italy., Cuccurullo D; Department of Laparoscopic and Robotic General Surgery, Monaldi Hospital, AORN dei Colli, Naples, Italy., Costamagna G; Fondazione Policlinico Universitario A. Gemelli IRCCS, Rome, Italy., Dallemagne B; Institute for Research Against Digestive Cancer (IRCAD), Strasbourg, France.; IHU-Strasbourg, Institute of Image-Guided Surgery, Strasbourg, France., Padoy N; ICube, University of Strasbourg, CNRS, c/o IHU-Strasbourg, 1, place de l'hôpital, 67000, Strasbourg, France.; IHU-Strasbourg, Institute of Image-Guided Surgery, Strasbourg, France.
Jazyk: angličtina
Zdroj: Surgical endoscopy [Surg Endosc] 2022 Nov; Vol. 36 (11), pp. 8379-8386. Date of Electronic Publication: 2022 Feb 16.
DOI: 10.1007/s00464-022-09112-1
Abstrakt: Background: A computer vision (CV) platform named EndoDigest was recently developed to facilitate the use of surgical videos. Specifically, EndoDigest automatically provides short video clips to effectively document the critical view of safety (CVS) in laparoscopic cholecystectomy (LC). The aim of the present study is to validate EndoDigest on a multicentric dataset of LC videos.
Methods: LC videos from 4 centers were manually annotated with the time of the cystic duct division and an assessment of CVS criteria. Incomplete recordings, bailout procedures and procedures with an intraoperative cholangiogram were excluded. EndoDigest leveraged predictions of deep learning models for workflow analysis in a rule-based inference system designed to estimate the time of the cystic duct division. Performance was assessed by computing the error in estimating the manually annotated time of the cystic duct division. To provide concise video documentation of CVS, EndoDigest extracted video clips showing the 2 min preceding and the 30 s following the predicted cystic duct division. The relevance of the documentation was evaluated by assessing CVS in automatically extracted 2.5-min-long video clips.
Results: 144 of the 174 LC videos from 4 centers were analyzed. EndoDigest located the time of the cystic duct division with a mean error of 124.0 ± 270.6 s despite the use of fluorescent cholangiography in 27 procedures and great variations in surgical workflows across centers. The surgical evaluation found that 108 (75.0%) of the automatically extracted short video clips documented CVS effectively.
Conclusions: EndoDigest was robust enough to reliably locate the time of the cystic duct division and efficiently video document CVS despite the highly variable workflows. Training specifically on data from each center could improve results; however, this multicentric validation shows the potential for clinical translation of this surgical data science tool to efficiently document surgical safety.
(© 2022. The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.)
Databáze: MEDLINE
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