Deep learning enables accurate diagnosis of acute cholecystitis and prediction of suppuration using noncontrast CT.

ABSTRACT: To develop a deep learning model using abdominal noncontrast computed tomography (CT) for diagnosing acute cholecystitis (AC) and predicting progression to acute suppurative cholecystitis (ASC). A total of 641 patients from three medical centers were retrospectively analyzed. Deep learning models were constructed for AC diagnosis and ASC prediction. Model interpretability was assessed using gradient-weighted class activation mapping (Grad-CAM) and t-distributed stochastic neighbor embedding. Performance was compared with radiomics models and radiologist assessments. The deep learning model achieved accuracies of 89.81% (internal) and 81.83% (external) for AC and 84.52% (internal) and 85.60% (external) for ASC prediction. Grad-CAM visualizations showed focus on gallbladder regions and surro

SUBMITTER: Chen BQ 

PROVIDER: S-EPMC12741419 | biostudies-literature | 2025 Dec

REPOSITORIES: biostudies-literature