Development of a multimodel predictive framework for gangrenous cholecystitis with SHAP-based interpretability

Background

Gangrenous Cholecystitis (GC) is a critical pathological type of acute cholecystitis that tends to induce gallbladder wall necrosis or perforation, thereby leading to localized or diffuse peritonitis and is associated with high morbidity and mortality. Due to its nonspecific clinical symptoms and strong heterogeneous manifestations, timely and accurate identification of GC preoperatively remains a clinical challenge. To address this unmet clinical need, we developed an interpretable machine learning (ML) model using routinely available preoperative variables to facilitate the early prediction of GC and support clinical decision-making.