{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,31]],"date-time":"2026-10-31T06:34:05Z","timestamp":1793428445998,"version":"4.5.0"},"reference-count":33,"publisher":"Springer Science and Business Media LLC","issue":"8","license":[{"start":{"date-parts":[[2026,1,12]],"date-time":"2026-01-12T00:00:00Z","timestamp":1768176000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2026,1,12]],"date-time":"2026-01-12T00:00:00Z","timestamp":1768176000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Abdom Radiol"],"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Purpose<\/jats:title>\n                    <jats:p>To develop and evaluate the diagnostic accuracy of a deep learning algorithm for automated gallstone detection on CT.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats: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.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>Ninety patients (mean age 64.9 \u00b1 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\u2013100.0) and specificity of 83.3% (95% CI: 70.0\u201396.7). 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 \u00b1 38.3 vs. 24.0 mL \u00b1 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.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>The algorithm detects gallstones in CT scans with high sensitivity, particularly for large, conspicuous stones.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1007\/s00261-025-05352-6","type":"journal-article","created":{"date-parts":[[2026,1,12]],"date-time":"2026-01-12T06:22:04Z","timestamp":1768198924000},"page":"4158-4167","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["Automated detection of gallbladder stones using a deep learning algorithm on computed tomography scans"],"prefix":"10.1007","volume":"51","author":[{"ORCID":"https:\/\/orcid.org\/0009-0007-1858-0030","authenticated-orcid":false,"given":"Manfred T.","family":"Meyer","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9921-5698","authenticated-orcid":false,"given":"Jakob","family":"Wasserthal","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4584-0623","authenticated-orcid":false,"given":"Joshy","family":"Cyriac","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5209-0576","authenticated-orcid":false,"given":"Shan","family":"Yang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6894-0507","authenticated-orcid":false,"given":"Elmar M.","family":"Merkle","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9949-0545","authenticated-orcid":false,"given":"Tobias","family":"Heye","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2026,1,12]]},"reference":[{"key":"5352_CR1","doi-asserted-by":"publisher","first-page":"423","DOI":"10.1038\/ncpgasthep0257","volume":"2","author":"F Lammert","year":"2005","unstructured":"Lammert F, Sauerbruch T (2005) Mechanisms of Disease: the genetic epidemiology of gallbladder stones. 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