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

Author

Listed:
  • Ayis Pyrros

    (Duly Health and Care, Department of Radiology
    University of Illinois Chicago)

  • 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
    Stanford University
    Microsoft Corporation)

  • 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)

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.

Suggested Citation

  • Ayis Pyrros & Stephen M. Borstelmann & Ramana Mantravadi & Zachary Zaiman & Kaesha Thomas & Brandon Price & Eugene Greenstein & Nasir Siddiqui & Melinda Willis & Ihar Shulhan & John Hines-Shah & Jeann, 2023. "Opportunistic detection of type 2 diabetes using deep learning from frontal chest radiographs," Nature Communications, Nature, vol. 14(1), pages 1-12, December.
    • Handle: RePEc:nat:natcom:v:14:y:2023:i:1:d:10.1038_s41467-023-39631-x
    DOI: 10.1038/s41467-023-39631-x
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    References listed on IDEAS

    as
    1. Chinmay Belthangady & Stefanos Giampanis & Ivana Jankovic & Will Stedden & Paula Alves & Stephanie Chong & Charlotte Knott & Beau Norgeot, 2022. "Causal deep learning reveals the comparative effectiveness of antihyperglycemic treatments in poorly controlled diabetes," Nature Communications, Nature, vol. 13(1), pages 1-10, December.
    2. Aasthaa Bansal & Patrick J. Heagerty, 2018. "A Tutorial on Evaluating the Time-Varying Discrimination Accuracy of Survival Models Used in Dynamic Decision Making," Medical Decision Making, , vol. 38(8), pages 904-916, November.
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