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

Ayis Pyrros (), Stephen M. Borstelmann, Ramana Mantravadi, Zachary Zaiman, Kaesha Thomas, Brandon Price, Eugene Greenstein, Nasir Siddiqui, Melinda Willis, Ihar Shulhan, John Hines-Shah, Jeanne M. Horowitz, Paul Nikolaidis, Matthew P. Lungren, Jorge Mario Rodríguez-Fernández, Judy Wawira Gichoya, Sanmi Koyejo, Adam E Flanders, Nishith Khandwala, Amit Gupta, John W. Garrett, Joseph Paul Cohen, Brian T. Layden, Perry J. Pickhardt and William Galanter
Additional contact information
Ayis Pyrros: Duly Health and Care, Department of Radiology
Stephen M. Borstelmann: University of Central Florida
Ramana Mantravadi: Brainnet, Inc.
Zachary Zaiman: Emory University
Kaesha Thomas: Emory University
Brandon Price: Florida State University
Eugene Greenstein: Duly Health and Care
Nasir Siddiqui: Duly Health and Care, Department of Radiology
Melinda Willis: Duly Health and Care, Department of Radiology
Ihar Shulhan: EPAM, Inc
John Hines-Shah: Duly Health and Care, Department of Radiology
Jeanne M. Horowitz: Northwestern University
Paul Nikolaidis: Northwestern University
Matthew P. Lungren: UCSF
Jorge Mario Rodríguez-Fernández: The University of Texas Medical Branch
Judy Wawira Gichoya: Emory University
Sanmi Koyejo: Stanford University
Adam E Flanders: Thomas Jefferson University
Nishith Khandwala: Bunkerhill
Amit Gupta: University Hospitals Cleveland Medical Center
John W. Garrett: University of Wisconsin
Joseph Paul Cohen: Stanford University
Brian T. Layden: University of Illinois Chicago
Perry J. Pickhardt: University of Wisconsin
William Galanter: University of Illinois Chicago

Nature Communications, 2023, vol. 14, issue 1, 1-12

Abstract: Abstract 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.

Date: 2023
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DOI: 10.1038/s41467-023-39631-x

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