ArticleSurgical endoscopy2026
Development, application and evaluation of an artificial intelligence (AI)-based platform (SurgSmart) for the automatic assessment of the critical view of safety (CVS) in laparoscopic cholecystectomy (LC).
Article in Surgical endoscopy, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
1 citing paper in PubMed.
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Authors and funding
21 authors.
Funding
Abstract
backgroundLaparoscopic Cholecystectomy (LC) is the standard surgical treatment for symptomatic benign gallbladder diseases. Bile Duct Injury (BDI) is a common and serious complication of LC. Critical View of Safety (CVS) has been proven crucial in preventing BDI. However, current attainment rate of CVS remains low. Recent advancements in AI offer an efficient method for this gap. This study aims to develop an intelligent surgical platform (SurgSmart) enabling real-time assessment of the Critical View of Safety (CVS), integrate it into routine surgical practice, and evaluate its performance and user acceptance based on intraoperative and post-operative feedback. MATERIALS AND
methodsA total of 377 LC videos from 17 hospitals were retrospectively collected for training, validation, and testing of the AI algorithm. The model's effectiveness was evaluated using accuracy, precision, recall, F1-score, and macro-average F1-score. Our platform was deployed in the operating rooms of three hospitals. From May to October 2024, we collected LC videos and surgical reports and assessing variations in CVS scores. Surgeons were surveyed to evaluate user satisfaction, surgical confidence and to gather suggestions.
resultsFor CVS I, II, and III the overall accuracy and macro-average F1-score are 0.91 and 0.72, 0.86 and 0.67, 0.73 and 0.70, respectively. The overall CVS scores for the three hospitals showed significant improvement after the deployment of platform (P < 0.01). Fifteen out of the eighteen surgeons who used our platform demonstrated overall improvement (P < 0.05). Surgeons' satisfaction was high, with recommendations including more adequate training and guidance as well as further improvements in model performance.
conclusionThis platform has demonstrated its feasibility for real-time and automated CVS assessment. Most surgeons improved after using our platform. Surgeons reported positive feedback and expressed hope for more adequate guidance and continuous improvements in model performance.
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Registered trials
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