Prospective Study of an Explainable AI Tool to Support Biliopancreatic Limb Selection in One-Anastomosis Gastric Bypass

This prospective observational cohort study will develop and externally validate a human-supervised, explainable artificial-intelligence decision-support model for analysing factors associated with biliopancreatic limb length selection during one-anastomosis gastric bypass (OAGB). The study will record three prespecified factors: the percentage of the laparoscopic instrument segment J-I covered by visceral or omental fat (P\_JI = 100 × FI/JI), the measured total small-bowel length (TSBL), and the abdominal integral index (AII = (CD/AC) + (CG/EG) + (JI/FI)). The study will evaluate weight-loss effectiveness, nutritional and malabsorptive outcomes, bile/acid reflux, major complications, and technical feasibility. During model development, the AI system will not autonomously assign a BPL length. The operating bariatric surgeon will make the clinical decision, and the model will be evaluated for calibration, external validity, factor contribution, safety constraints, and abstention.