Computer Vision Analysis of Intraoperative Video: Automated Recognition of Operative Steps in Laparoscopic Sleeve Gastrectomy
Name
nihms-1585507.pdf
Description
Published version
Size
478.61 KB
Format
Adobe PDF
Checksum (MD5)
07862e779c9edfd885bb106258890d4e
Author(s)
Hashimoto, Daniel A
Rosman, Guy
Witkowski, Elan R
Stafford, Caitlin
Navarette-Welton, Allison J
Rattner, David W
Lillemoe, Keith D
Rus, Daniela L
Meireles, Ozanan R
Date Issued
2019
Journal
Annals of Surgery
Publisher
Ovid Technologies (Wolters Kluwer Health)
Version
Author's final manuscript
Abstract
© 2019 Wolters Kluwer Health, Inc. All rights reserved. Objective(s):To develop and assess AI algorithms to identify operative steps in laparoscopic sleeve gastrectomy (LSG).Background:Computer vision, a form of artificial intelligence (AI), allows for quantitative analysis of video by computers for identification of objects and patterns, such as in autonomous driving.Methods:Intraoperative video from LSG from an academic institution was annotated by 2 fellowship-Trained, board-certified bariatric surgeons. Videos were segmented into the following steps: 1) port placement, 2) liver retraction, 3) liver biopsy, 4) gastrocolic ligament dissection, 5) stapling of the stomach, 6) bagging specimen, and 7) final inspection of staple line. Deep neural networks were used to analyze videos. Accuracy of operative step identification by the AI was determined by comparing to surgeon annotations.Results:Eighty-eight cases of LSG were analyzed. A random 70% sample of these clips was used to train the AI and 30% to test the AI's performance. Mean concordance correlation coefficient for human annotators was 0.862, suggesting excellent agreement. Mean (±SD) accuracy of the AI in identifying operative steps in the test set was 82%±4% with a maximum of 85.6%.Conclusions:AI can extract quantitative surgical data from video with 85.6% accuracy. This suggests operative video could be used as a quantitative data source for research in intraoperative clinical decision support, risk prediction, or outcomes studies.
MIT Department
Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory
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Creative Commons Attribution-Noncommercial-Share Alike
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DOI of Published Version
https://doi.org/10.1097/SLA.0000000000003460
