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

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 mod...

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Detalles Bibliográficos
Autores principales: Pyrros, Ayis, Borstelmann, Stephen M., Mantravadi, Ramana, Zaiman, Zachary, Thomas, Kaesha, Price, Brandon, Greenstein, Eugene, Siddiqui, Nasir, Willis, Melinda, Shulhan, Ihar, Hines-Shah, John, Horowitz, Jeanne M., Nikolaidis, Paul, Lungren, Matthew P., Rodríguez-Fernández, Jorge Mario, Gichoya, Judy Wawira, Koyejo, Sanmi, Flanders, Adam E, Khandwala, Nishith, Gupta, Amit, Garrett, John W., Cohen, Joseph Paul, Layden, Brian T., Pickhardt, Perry J., Galanter, William
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2023
Materias:
Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10328953/
https://www.ncbi.nlm.nih.gov/pubmed/37419921
http://dx.doi.org/10.1038/s41467-023-39631-x

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10328953/
https://www.ncbi.nlm.nih.gov/pubmed/37419921
http://dx.doi.org/10.1038/s41467-023-39631-x

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