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; William Galanter

doi:10.1038/s41467-023-39631-x

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 GuptaJohn W. Garrett, Joseph Paul Cohen, Brian T. Layden, Perry J. Pickhardt, William Galanter

Research output: Contribution to journal › Article › peer-review

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.

Original language	English (US)
Article number	4039
Journal	Nature communications
Volume	14
Issue number	1
DOIs	https://doi.org/10.1038/s41467-023-39631-x
State	Published - Dec 2023
Externally published	Yes

ASJC Scopus subject areas

General Chemistry
General Biochemistry, Genetics and Molecular Biology
General Physics and Astronomy

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Pyrros, A., Borstelmann, S. M., Mantravadi, R., Zaiman, Z., Thomas, K., Price, B., Greenstein, E., Siddiqui, N., Willis, M., Shulhan, I., Hines-Shah, J., Horowitz, J. M., Nikolaidis, P., Lungren, M. P., Rodríguez-Fernández, J. M., Gichoya, J. W., Koyejo, S., Flanders, A. E., Khandwala, N., ... Galanter, W. (2023). Opportunistic detection of type 2 diabetes using deep learning from frontal chest radiographs. Nature communications, 14(1), Article 4039. https://doi.org/10.1038/s41467-023-39631-x

Pyrros, A, Borstelmann, SM, Mantravadi, R, Zaiman, Z, Thomas, K, Price, B, Greenstein, E, Siddiqui, N, Willis, M, Shulhan, I, Hines-Shah, J, Horowitz, JM, Nikolaidis, P, Lungren, MP, Rodríguez-Fernández, JM, Gichoya, JW, Koyejo, S, Flanders, AE, Khandwala, N, Gupta, A, Garrett, JW, Cohen, JP, Layden, BT, Pickhardt, PJ & Galanter, W 2023, 'Opportunistic detection of type 2 diabetes using deep learning from frontal chest radiographs', Nature communications, vol. 14, no. 1, 4039. https://doi.org/10.1038/s41467-023-39631-x

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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{\textquoteright} potential for enhanced T2D screening.",

author = "Ayis Pyrros and Borstelmann, {Stephen M.} and Ramana Mantravadi and Zachary Zaiman and Kaesha Thomas and Brandon Price and Eugene Greenstein and Nasir Siddiqui and Melinda Willis and Ihar Shulhan and John Hines-Shah and Horowitz, {Jeanne M.} and Paul Nikolaidis and Lungren, {Matthew P.} and Rodr{\'i}guez-Fern{\'a}ndez, {Jorge Mario} and Gichoya, {Judy Wawira} and Sanmi Koyejo and Flanders, {Adam E.} and Nishith Khandwala and Amit Gupta and Garrett, {John W.} and Cohen, {Joseph Paul} and Layden, {Brian T.} and Pickhardt, {Perry J.} and William Galanter",

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AU - Price, Brandon

AU - Greenstein, Eugene

AU - Siddiqui, Nasir

AU - Willis, Melinda

AU - Shulhan, Ihar

AU - Hines-Shah, John

AU - Horowitz, Jeanne M.

AU - Nikolaidis, Paul

AU - Lungren, Matthew P.

AU - Rodríguez-Fernández, Jorge Mario

AU - Gichoya, Judy Wawira

AU - Koyejo, Sanmi

AU - Flanders, Adam E.

AU - Khandwala, Nishith

AU - Gupta, Amit

AU - Garrett, John W.

AU - Cohen, Joseph Paul

AU - Layden, Brian T.

AU - Pickhardt, Perry J.

AU - Galanter, William

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

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