Acute Cholecystitis Diagnosis in the Emergency Department: An Artificial Intelligence-based Approach

Abstract

Objectives This study aimed to assess the diagnostic performance of a support vector machine (SVM) algorithm for acute cholecystitis and evaluate its effectiveness in accurately diagnosing this condition. Methods Using a retrospective analysis of patient data from a single center, individuals with abdominal pain lasting one week or less were included. The SVM model was trained and optimized using standard procedures. Model performance was assessed through sensitivity, specificity, accuracy, and AUC-ROC, with probability calibration evaluated using the Brier score. Results Among 534 patients, 198 (37.07%) were diagnosed with acute cholecystitis. The SVM model showed balanced performance, with a sensitivity of 83.08% (95% CI: 71.73–91.24%), a specificity of 80.21% (95% CI: 70.83–87.64%), and an accuracy of 81.37% (95% CI: 74.48–87.06%). The positive predictive value (PPV) was 73.97% (95% CI: 65.18–81.18%), the negative predictive value (NPV) was 87.50% (95% CI: 80.19–92.37%), and the AUC-ROC was 0.89 (95% CI: 0.85 to 0.93). The Brier score indicated well-calibrated probability estimates. Conclusion The SVM algorithm demonstrated promising potential for accurately diagnosing acute cholecystitis. Further refinement and validation are needed to enhance its reliability in clinical practice.