{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,31]],"date-time":"2026-10-31T22:11:58Z","timestamp":1793484718643,"version":"4.5.0"},"reference-count":38,"publisher":"Oxford University Press (OUP)","issue":"3","license":[{"start":{"date-parts":[[2026,4,3]],"date-time":"2026-04-03T00:00:00Z","timestamp":1775174400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc\/4.0\/"}],"funder":[{"DOI":"10.13039\/100000001","name":"National Science Foundation","doi-asserted-by":"publisher","award":["1922658"],"award-info":[{"award-number":["1922658"]}],"id":[{"id":"10.13039\/100000001","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Society of Abdominal Radiology GE Healthcare Research and Innovation"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2026,5,11]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Background<\/jats:title>\n                    <jats:p>Acute pancreatitis (AP) is a common gastrointestinal disease with a rising global incidence. While most cases are mild, severe AP (SAP) carries high mortality. Early and accurate severity prediction facilitates management optimization. However, existing clinical severity prediction models, such as Bedside Index of Severity in Acute Pancreatitis (BISAP) and Modified CT Severity Index (mCTSI), have modest accuracy and often rely on data unavailable at admission.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Purpose<\/jats:title>\n                    <jats:p>This study proposes a deep learning (DL) model to predict AP severity using abdominal contrast-enhanced CT scans acquired within 24\u2009hours of admission.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Materials and Methods<\/jats:title>\n                    <jats:p>We collected 10\u00a0130 studies from 8335 patients across a multi-site U.S. health system and 3488 studies from public datasets. The DL model was trained in 2 stages: (1) self-supervised pretraining on 11\u00a0896 unlabeled studies and (2) fine-tuning on 550 labeled studies. Performance was evaluated against mCTSI on a hold-out internal test set (n\u2009=\u2009100 patients) and against both mCTSI and BISAP on an external Hungarian AP registry (n\u2009=\u2009518 patients).<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>On the internal test set, the model achieved areas under receiver operating curve (AUROCs) of 0.888 (95% CI: 0.800-0.960, sensitivity 0.50, specificity 0.92) for SAP and 0.888 (95% CI: 0.819-0.946, sensitivity 0.75, specificity 0.85) for mild AP (MAP), outperforming mCTSI (P\u2009=\u2009.002). External validation showed AUROCs of 0.887 (95% CI: 0.825-0.941, sensitivity 0.73, specificity 0.88) for SAP and 0.858 (95% CI: 0.826-0.888, sensitivity 0.38, specificity 0.98) for MAP, surpassing mCTSI (P\u2009=\u2009.024) and BISAP (P\u2009=\u2009.002). In retrospective triage analysis, the model correctly identified 50%-73% of patients who progressed to SAP and 40%-73% of those with MAP.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>The proposed DL model achieved performance comparable to or better than established prognostic tools and maintained robust external performance. These findings suggest that AI-assisted CT analysis may support early, automated risk stratification of AP.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1093\/radadv\/umag020","type":"journal-article","created":{"date-parts":[[2026,4,2]],"date-time":"2026-04-02T11:45:33Z","timestamp":1775130333000},"source":"Crossref","is-referenced-by-count":2,"title":["Deep learning-based prediction of acute pancreatitis severity from abdominal CT with multicenter external validation"],"prefix":"10.1093","volume":"3","author":[{"given":"Yanqi","family":"Xu","sequence":"first","affiliation":[{"name":"Center for Data Science, New York University , New York, New York, 10011,","place":["United States"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Brigitta","family":"Teutsch","sequence":"additional","affiliation":[{"name":"Centre for Translational Medicine, Semmelweis University , Budapest, 1085,","place":["Hungary"]},{"name":"Department of Radiology, Medical Imaging Centre, Semmelweis University , Budapest, 1082,","place":["Hungary"]},{"name":"Institute for Translational Medicine, University of P\u00e9cs , P\u00e9cs, 7624,","place":["Hungary"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Weicheng","family":"Zeng","sequence":"additional","affiliation":[{"name":"Center for Data Science, New York University , New York, New York, 10011,","place":["United States"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yang","family":"Hu","sequence":"additional","affiliation":[{"name":"Gallatin School of Individualized Study, New York University , New York, New York , 10003,","place":["United States"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Shikhar","family":"Rastogi","sequence":"additional","affiliation":[{"name":"Center for Data Science, New York University , New York, New York, 10011,","place":["United States"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Emmy Yuebi","family":"Hu","sequence":"additional","affiliation":[{"name":"Department of Radiology, NYU Langone Health , New York, New York, 10016,","place":["United States"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Isabella","family":"DeGregorio","sequence":"additional","affiliation":[{"name":"Cooper Medical School of Rowan University , Camden, New Jersey, 08103,","place":["United States"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Wan Fung","family":"Chui","sequence":"additional","affiliation":[{"name":"Department of Radiology, NYU Langone Health , New York, New York, 10016,","place":["United States"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Benjamin I","family":"Richter","sequence":"additional","affiliation":[{"name":"Division of Gastroenterology & Advanced Endoscopy, Department of Medicine, Rutgers New Jersey Medical School Newark, New Jersey, 07103,","place":["United 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1105,","place":["Hungary"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"N\u00e1ndor","family":"Faluhelyi","sequence":"additional","affiliation":[{"name":"Department of Medical Imaging, University of P\u00e9cs , P\u00e9cs, 7624,","place":["Hungary"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"\u00c1ron","family":"Vincze","sequence":"additional","affiliation":[{"name":"Division of Gastroenterology, First Department of Medicine, University of P\u00e9cs , P\u00e9cs, 7624,","place":["Hungary"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Katalin","family":"M\u00e1rta","sequence":"additional","affiliation":[{"name":"Centre for Translational Medicine, Semmelweis University , Budapest, 1085,","place":["Hungary"]},{"name":"Institute of Pancreatic Diseases, Semmelweis University , Budapest, 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