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Full-Text Articles in Diseases
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
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
Department of Radiology Faculty Papers
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 …
Covid-19 Related Biliary Injury: A Review Of Recent Literature, Sujani Yadlapati, Simone A. Jarrett, Daniel Baik, Adib Chaaya
Covid-19 Related Biliary Injury: A Review Of Recent Literature, Sujani Yadlapati, Simone A. Jarrett, Daniel Baik, Adib Chaaya
COVID-19 Papers, Posters, and Presentations
Since its emergence in 2019, it has become apparent that coronavirus 2019 (COVID-19) infection can result in multi systemic involvement. In addition to pulmonary symptoms, hepatobiliary involvement has been widely reported. Extent of hepatic involvement ranges from minor elevation in liver function tests (LFTs) to significant hepatocellular or cholestatic injury. In majority of cases, resolution of hepatic injury or improvement in LFTs is noted as patients recover from COVID-19 infection. However, severe biliary tract injury progressing to liver failure has been reported in patients requiring prolonged intensive care unit stay or mechanical ventilation. Due to the timing of its presentation, …