Deep learning-based prediction of acute pancreatitis severity from abdominal CT with multicenter external validation.

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Title: Deep learning-based prediction of acute pancreatitis severity from abdominal CT with multicenter external validation.
Authors: Xu Y; Center for Data Science, New York University, New York, New York, 10011, United States., Teutsch B; Centre for Translational Medicine, Semmelweis University, Budapest, 1085, Hungary.; Department of Radiology, Medical Imaging Centre, Semmelweis University, Budapest, 1082, Hungary.; Institute for Translational Medicine, University of Pécs, Pécs, 7624, Hungary., Zeng W; Center for Data Science, New York University, New York, New York, 10011, United States., Hu Y; Gallatin School of Individualized Study, New York University, New York, New York , 10003, United States., Rastogi S; Center for Data Science, New York University, New York, New York, 10011, United States., Hu EY; Department of Radiology, NYU Langone Health, New York, New York, 10016, United States., DeGregorio I; Cooper Medical School of Rowan University, Camden, New Jersey, 08103, United States., Chui WF; Department of Radiology, NYU Langone Health, New York, New York, 10016, United States., Richter BI; Division of Gastroenterology & Advanced Endoscopy, Department of Medicine, Rutgers New Jersey Medical SchoolNewark, New Jersey, 07103, United States., Cummings R; Department of Radiology, NYU Langone Health, New York, New York, 10016, United States., Goldberg JE; Department of Radiology, NYU Langone Health, New York, New York, 10016, United States., Mathieu E; Department of Radiology, NYU Langone Health, New York, New York, 10016, United States., Asare BA; Department of Radiology, NYU Langone Health, New York, New York, 10016, United States., Hegedűs P; Department of Radiology, Medical Imaging Centre, Semmelweis University, Budapest, 1082, Hungary., Gurza KB; Department of Radiology, Medical Imaging Centre, Semmelweis University, Budapest, 1082, Hungary., Szabó IV; Department of Radiology, Medical Imaging Centre, Semmelweis University, Budapest, 1082, Hungary., Tarján H; Department of Radiology, Medical Imaging Centre, Semmelweis University, Budapest, 1082, Hungary., Szentesi A; Centre for Translational Medicine, Semmelweis University, Budapest, 1085, Hungary.; Institute for Translational Medicine, University of Pécs, Pécs, 7624, Hungary., Borbély R; Centre for Translational Medicine, Semmelweis University, Budapest, 1085, Hungary.; Department of Medical Imaging, Bajcsy-Zsilinszky Hospital and Clinic, Budapest, 1105, Hungary., Molnár D; Department of Medical Imaging, Bajcsy-Zsilinszky Hospital and Clinic, Budapest, 1105, Hungary., Faluhelyi N; Department of Medical Imaging, University of Pécs, Pécs, 7624, Hungary., Vincze Á; Division of Gastroenterology, First Department of Medicine, University of Pécs, Pécs, 7624, Hungary., Márta K; Centre for Translational Medicine, Semmelweis University, Budapest, 1085, Hungary.; Institute of Pancreatic Diseases, Semmelweis University, Budapest, 1083, Hungary., Hegyi P; Centre for Translational Medicine, Semmelweis University, Budapest, 1085, Hungary.; Institute for Translational Medicine, University of Pécs, Pécs, 7624, Hungary.; Institute of Pancreatic Diseases, Semmelweis University, Budapest, 1083, Hungary.; Translational Pancreatology Research Group, Interdisciplinary Centre of Excellence for Research Development and Innovation, University of Szeged, Szeged, 6720, Hungary., Lei Q; Center for Data Science, New York University, New York, New York, 10011, United States., Gonda T; Division of Gastroenterology and Hepatology, Department of Medicine, NYU Langone Health, New York, New York, 10016, United States., Huang C; Department of Radiology, NYU Langone Health, New York, New York, 10016, United States., Shen Y; Center for Data Science, New York University, New York, New York, 10011, United States.; Department of Radiology, NYU Langone Health, New York, New York, 10016, United States.
Source: Radiology advances [Radiol Adv] 2026 Apr 03; Vol. 3 (3), pp. umag020. Date of Electronic Publication: 2026 Apr 03 (Print Publication: 2026).
Publication Type: Journal Article
Journal Info: Publisher: Oxford University Press Country of Publication: England NLM ID: 9918840888906676 Publication Model: eCollection Cited Medium: Internet ISSN: 2976-9337 (Electronic) Linking ISSN: 29769337 NLM ISO Abbreviation: Radiol Adv Subsets: PubMed not MEDLINE
Database: MEDLINE Ultimate
Description
ISSN:2976-9337
DOI:10.1093/radadv/umag020