Computer Vision Analysis of Intraoperative Video: Automated Recognition of Operative Steps in Laparoscopic Sleeve Gastrectomy

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Abstract

To develop and assess AI algorithms to identify operative steps in laparoscopic sleeve gastrectomy (LSG). 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. Intraoperative video from LSG from an academic institution were annotated by two 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. 88 cases of LSG were analyzed. A random 70% sample of these clips were 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%. 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. The goal of this study was to develop and assess AI algorithms to identify operative steps in laparoscopic sleeve gastrectomy (LSG). 88 videos were analyzed. Mean accuracy of the algorithms was 82% ± 4% in identifying steps of LSG.

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EndoNet: A Deep Architecture for Recognition Tasks on Laparoscopic Videos.

Nicolas Padoy, Michel De Mathelin, Jacques Marescaux … (2017)

Surgical workflow recognition has numerous potential medical applications, such as the automatic indexing of surgical video databases and the optimization of real-time operating room scheduling, among others. As a result, surgical phase recognition has been studied in the context of several kinds of surgeries, such as cataract, neurological, and laparoscopic surgeries. In the literature, two types of features are typically used to perform this task: visual features and tool usage signals. However, the used visual features are mostly handcrafted. Furthermore, the tool usage signals are usually collected via a manual annotation process or by using additional equipment. In this paper, we propose a novel method for phase recognition that uses a convolutional neural network (CNN) to automatically learn features from cholecystectomy videos and that relies uniquely on visual information. In previous studies, it has been shown that the tool usage signals can provide valuable information in performing the phase recognition task. Thus, we present a novel CNN architecture, called EndoNet, that is designed to carry out the phase recognition and tool presence detection tasks in a multi-task manner. To the best of our knowledge, this is the first work proposing to use a CNN for multiple recognition tasks on laparoscopic videos. Experimental comparisons to other methods show that EndoNet yields state-of-the-art results for both tasks.

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Complementing Operating Room Teaching With Video-Based Coaching.

Yue-Yung Hu, Laura M Mazer, Steven Yule … (2016)

Surgical expertise demands technical and nontechnical skills. Traditionally, surgical trainees acquired these skills in the operating room; however, operative time for residents has decreased with duty hour restrictions. As in other professions, video analysis may help maximize the learning experience.