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Implementation of Artificial Intelligence-Based Computer Vision Model for Sleeve Gastrectomy: Experience in One
1Division of General Surgery, Bariatric Unit, Tel Aviv Medical Center, Affiliated to Sackler Faculty of Medicine, Tel Aviv University, 6, Weizman St., Tel Aviv, Israel. danitd.75@gmail.com.
Obesity Surgery
|January 5, 2024
Summary
An AI model accurately annotated four of five safety milestones during sleeve gastrectomy (SG) procedures. This technology offers objective performance measures to enhance surgical quality and patient safety in bariatric surgery.
Area of Science:
- Minimally Invasive Surgery
- Artificial Intelligence in Medicine
- Surgical Video Analysis
Background:
- Sleeve gastrectomy (SG) is a prevalent bariatric procedure.
- Artificial intelligence (AI) offers potential for automated analysis of surgical videos.
- Real-world implementation of AI for SG video annotation is explored.
Purpose of the Study:
- To implement and validate an AI-based computer vision model for sleeve gastrectomy (SG).
- To assess the accuracy of AI-driven annotations for surgical safety milestones.
- To evaluate the potential of AI in improving surgical quality and efficiency.
Main Methods:
- Retrospective analysis of 49 sleeve gastrectomy videos using an AI platform.
- Comparison of AI-generated safety milestone annotations with bariatric surgeon assessments.
- Data retrieval from the bariatric unit registry for patient information.
Main Results:
- The AI model demonstrated high accuracy in annotating key safety milestones: bougie insertion (100%), distance from pylorus (100%), fundus mobilization (100%), and distance from esophagus (100%).
- Accuracy for parallel to lesser curvature was 98%.
- Intraoperative complications included hemorrhage (n=4) and parenchymal injury (n=1).
Conclusions:
- The AI model provides automated sleeve gastrectomy video analysis with high accuracy for most safety milestones.
- Objective performance measures from AI can enhance surgical quality and efficiency.
- Further studies with larger cohorts are needed for standardization and clinical outcome correlations.
