Implementation of Artificial Intelligence-Based Computer Vision Model for Sleeve Gastrectomy: Experience in One Tertiary Center.

INTRODUCTION

Sleeve gastrectomy (SG) is the most common metabolic and bariatric procedure performed. Leveraging artificial intelligence (AI) for automated real-time data structuring and annotations of surgical videos has immense potential of clinical applications. This study presents initial real-world implementation of AI-based computer vision model in sleeve gastrectomy (SG) and external validation of accuracy of safety milestone annotations.

METHODS

A retrospective single-center study of 49 consecutive SG videos was captured and analyzed by the AI platform (December 2020-August 2023). A bariatric surgeon viewed all videos and assessed safety milestones adherence, compared to the AI annotations. Patients' data were retrieved from the bariatric unit registry.

RESULTS

SG total duration was 47.5 min (interquartile range 36-64). Main steps included preparation (12.2%), dissection of the greater curvature (30.8%), gastric transection (28.5%), specimen extraction (7.2%), and final inspection (14.4%). Out of body time comprised 6.9% of the total video. Safety milestones components and AI-surgeon agreements included the following: bougie insertion (100%), distance from pylorus ≥ 2 cm (100%), parallel to lesser curvature (98%), fundus mobilization (100%), and distance from esophagus ≥ 1 cm (true-100%, false-13.6%; kappa coefficient 0.2, p = 0.006). Intraoperative complications included notable hemorrhage (n = 4) and parenchymal injury (n = 1).

CONCLUSIONS

The AI model provides a fully automated SG video analysis. Outcomes suggest its accuracy in four of five safety milestone annotations. This data is valuable, as it reflects objective performance measures which can help us improve the surgical quality and efficiency of SG. Larger cohorts will enable SG standardization and clinical correlations with outcomes, aiming to improve patients' safety.