دورية أكاديمية
أعرض في EDS

Opportunistic detection of type 2 diabetes using deep learning from frontal chest radiographs.

التفاصيل البيبلوغرافية
العنوان: Opportunistic detection of type 2 diabetes using deep learning from frontal chest radiographs.
المؤلفون: Pyrros A; Duly Health and Care, Department of Radiology, Downers Grove, IL, USA. ayis@uic.edu.; Department of Biomedical and Health Information Sciences, University of Illinois Chicago, Chicago, IL, USA. ayis@uic.edu., Borstelmann SM; Department of Radiology, University of Central Florida, Orlando, FL, USA., Mantravadi R; Brainnet, Inc., West Harrison, NY, USA., Zaiman Z; Department of Radiology, Emory University, Atlanta, GA, USA., Thomas K; Department of Radiology, Emory University, Atlanta, GA, USA., Price B; Department of Radiology, Florida State University, Tallahassee, FL, USA., Greenstein E; Department of Cardiology, Duly Health and Care, Downers Grove, IL, USA., Siddiqui N; Duly Health and Care, Department of Radiology, Downers Grove, IL, USA., Willis M; Duly Health and Care, Department of Radiology, Downers Grove, IL, USA., Shulhan I; EPAM, Inc, Newtown, PA, USA., Hines-Shah J; Duly Health and Care, Department of Radiology, Downers Grove, IL, USA., Horowitz JM; Department of Radiology, Northwestern University, Chicago, IL, USA., Nikolaidis P; Department of Radiology, Northwestern University, Chicago, IL, USA., Lungren MP; Department of Biomedical and Health Information Sciences, UCSF, San Francisco, CA, USA.; Center for Artificial Intelligence in Medicine, Stanford University, Stanford, CA, USA.; Microsoft, Microsoft Corporation, Redmond, USA., Rodríguez-Fernández JM; Department of Neurology, The University of Texas Medical Branch, Galveston, TX, USA., Gichoya JW; Department of Radiology, Emory University, Atlanta, GA, USA., Koyejo S; Department of Computer Science, Stanford University, Stanford, CA, USA., Flanders AE; Department of Radiology, Thomas Jefferson University, Philadelphia, PA, USA., Khandwala N; Bunkerhill, Palo Alto, CA, USA., Gupta A; Department of Radiology, University Hospitals Cleveland Medical Center, Cleveland, OH, USA., Garrett JW; Department of Radiology, University of Wisconsin, Madison, WI, USA., Cohen JP; Center for Artificial Intelligence in Medicine, Stanford University, Stanford, CA, USA., Layden BT; Department of Medicine, University of Illinois Chicago, Chicago, IL, USA., Pickhardt PJ; Department of Radiology, University of Wisconsin, Madison, WI, USA., Galanter W; Department of Medicine, University of Illinois Chicago, Chicago, IL, USA.
المصدر: Nature communications [Nat Commun] 2023 Jul 07; Vol. 14 (1), pp. 4039. Date of Electronic Publication: 2023 Jul 07.
نوع المنشور: Journal Article; Research Support, N.I.H., Extramural; Research Support, Non-U.S. Gov't; Research Support, U.S. Gov't, Non-P.H.S.
اللغة: English
بيانات الدورية: Publisher: Nature Pub. Group Country of Publication: England NLM ID: 101528555 Publication Model: Electronic Cited Medium: Internet ISSN: 2041-1723 (Electronic) Linking ISSN: 20411723 NLM ISO Abbreviation: Nat Commun Subsets: MEDLINE
أسماء مطبوعة: Original Publication: [London] : Nature Pub. Group
مواضيع طبية MeSH: Diabetes Mellitus, Type 2*/diagnostic imaging , Deep Learning*, Humans ; Radiography, Thoracic/methods ; Prospective Studies ; Radiography
مستخلص: Deep learning (DL) models can harness electronic health records (EHRs) to predict diseases and extract radiologic findings for diagnosis. With ambulatory chest radiographs (CXRs) frequently ordered, we investigated detecting type 2 diabetes (T2D) by combining radiographic and EHR data using a DL model. Our model, developed from 271,065 CXRs and 160,244 patients, was tested on a prospective dataset of 9,943 CXRs. Here we show the model effectively detected T2D with a ROC AUC of 0.84 and a 16% prevalence. The algorithm flagged 1,381 cases (14%) as suspicious for T2D. External validation at a distinct institution yielded a ROC AUC of 0.77, with 5% of patients subsequently diagnosed with T2D. Explainable AI techniques revealed correlations between specific adiposity measures and high predictivity, suggesting CXRs' potential for enhanced T2D screening.
(© 2023. The Author(s).)
التعليقات: Erratum in: Nat Commun. 2024 Jun 6;15(1):4817. doi: 10.1038/s41467-024-49184-2. (PMID: 38844459)
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معلومات مُعتمدة: R01 DK104927 United States DK NIDDK NIH HHS; 75N92020C00021 United States HL NHLBI NIH HHS; I01 BX000382 United States BX BLRD VA; 75N92020C00008 United States HL NHLBI NIH HHS; P30 DK020595 United States DK NIDDK NIH HHS; R01 LM013151 United States LM NLM NIH HHS
تواريخ الأحداث: Date Created: 20230707 Date Completed: 20230710 Latest Revision: 20240609
رمز التحديث: 20240609
مُعرف محوري في PubMed: PMC10328953
DOI: 10.1038/s41467-023-39631-x
PMID: 37419921
قاعدة البيانات: MEDLINE
الوصف
تدمد:2041-1723
DOI:10.1038/s41467-023-39631-x