Purpose <p>To develop and evaluate the diagnostic accuracy of a deep learning algorithm for automated gallstone detection on CT.</p> Methods <p>This retrospective single-center study included randomly selected CT scans from January 2018 to June 2019: 493 gallstone-positive, 470 gallstone-negative, and 180 post-cholecystectomy scans according to the original report (reference standard). Deep learning models were trained on segmented gallbladders (452 scans) and gallstone bounding-box labels (400 scans), randomly selected and manually labeled. All models used 5-fold cross-validation. Gallstone characteristics were collected (e.g., quantity, size, conspicuity). Where available, ultrasound and MRI reports within six months of CT were reviewed for cholecystolithiasis. Diagnostic performance was evaluated on a test set of 90 scans (30 per category). Statistical analyses included Dice coefficient, sensitivity, specificity, and ROC analysis.</p> Results <p>Ninety patients (mean age 64.9 ± 17.9 years; 46 women) were evaluated. The segmentation algorithm achieved a median Dice score of 94.4% (IQR 4.5%). The detection pipeline identified gallstone-positive scans with a sensitivity of 96.7% (95% CI: 90.2–100.0) and specificity of 83.3% (95% CI: 70.0–96.7). Sensitivity was highest for hyperdense (97.3%) and large stones (&gt;10 mm, 97.1%) but lower for gaseous (71.4%), isodense (78.6%), and small stones (&lt;5 mm, 88.9%). Cholecystectomy was correctly identified in 76.7% of cases. Gallstone presence correlated with larger gallbladder volumes (mean 42.8 mL ± 38.3 vs. 24.0 mL ± 20.7, p &lt; 0.001). Ultrasound/MRI comparisons (available for 178 [18.5%] of CTs with a gallbladder) showed low false-positive (0.6%) and false-negative (4.5%) rates for CT.</p> Conclusion <p>The algorithm detects gallstones in CT scans with high sensitivity, particularly for large, conspicuous stones.</p>