Abdominal radiology (New York) · 2026
Automated detection of gallbladder stones using a deep learning algorithm on computed tomography scans.
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Meyer Manfred T, Wasserthal Jakob, Cyriac Joshy, Yang Shan, Merkle Elmar M, Heye Tobias
Аннотация
PURPOSE: To develop and evaluate the diagnostic accuracy of a deep learning algorithm for automated gallstone detection on CT.
METHODS: 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.
RESULTS: 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 (>10 mm, 97.1%) but lower for gaseous (71.4%), isodense (78.6%), and small stones (<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 < 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.
CONCLUSION: The algorithm detects gallstones in CT scans with high sensitivity, particularly for large, conspicuous stones.
METHODS: 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.
RESULTS: 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 (>10 mm, 97.1%) but lower for gaseous (71.4%), isodense (78.6%), and small stones (<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 < 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.
CONCLUSION: The algorithm detects gallstones in CT scans with high sensitivity, particularly for large, conspicuous stones.