Development and Validation of an Explainable Machine Learning Model for Gangrenous Cholecystitis Prediction: A Multicenter Retrospective Study

In our multicenter cohorts, this interpretable machine learning model showed good discrimination for preoperative risk stratification of gangrenous cholecystitis and acceptable generalizability between centers. By integrating clinical, laboratory, and imaging features and providing explainability, the approach may assist perioperative decision-making when used alongside clinical judgment. Prospective, multicenter evaluations and clinical impact studies are warranted before routine clinical adoption.