L
Lea Azour
Researcher at Icahn School of Medicine at Mount Sinai
Publications - 34
Citations - 488
Lea Azour is an academic researcher from Icahn School of Medicine at Mount Sinai. The author has contributed to research in topics: Medicine & Internal medicine. The author has an hindex of 6, co-authored 22 publications receiving 216 citations. Previous affiliations of Lea Azour include University of York & New York University.
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Increased incidence of barotrauma in patients with COVID-19 on invasive mechanical ventilation
Georgeann McGuinness,Chenyang Zhan,Noah Rosenberg,Lea Azour,Maj Wickstrom,Derek Mason,Kristen M Thomas,William Moore +7 more
TL;DR: Patients with COVID-19 infection and invasive mechanical ventilation had a higher rate of barotrauma than patients with acute respiratory distress syndrome and patients without COVID -19 infection.
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An artificial intelligence system for predicting the deterioration of COVID-19 patients in the emergency department
Farah E. Shamout,Yiqiu Shen,Nan Wu,Aakash Kaku,Jungkyu Park,Taro Makino,Taro Makino,Stanisław Jastrzębski,Stanisław Jastrzębski,Jan Witowski,Duo Wang,Ben Zhang,Siddhant Dogra,Meng Cao,Narges Razavian,Narges Razavian,David Kudlowitz,Lea Azour,William Moore,Yvonne W. Lui,Yindalon Aphinyanaphongs,Carlos Fernandez-Granda,Krzysztof J. Geras,Krzysztof J. Geras +23 more
TL;DR: In this article, the authors proposed a data-driven approach for automatic prediction of deterioration risk using a deep neural network that learns from chest X-ray images and a gradient boosting model.
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Estimation of cardiovascular risk on routine chest CT: Ordinal coronary artery calcium scoring as an accurate predictor of Agatston score ranges
TL;DR: Visual assessment of CAC on non-gated routine chest CT accurately predicts Agatston score ranges, including the zero score, in ECG-gator CT, and is useful to convey important information on cardiovascular risk, particularly premature atherosclerosis in younger patients.
Posted Content
An artificial intelligence system for predicting the deterioration of COVID-19 patients in the emergency department
Farah E. Shamout,Yiqiu Shen,Nan Wu,Aakash Kaku,Jungkyu Park,Taro Makino,Taro Makino,Stanisław Jastrzębski,Stanisław Jastrzębski,Jan Witowski,Duo Wang,Ben Zhang,Siddhant Dogra,Meng Cao,Narges Razavian,Narges Razavian,David Kudlowitz,Lea Azour,William Moore,Yvonne W. Lui,Yindalon Aphinyanaphongs,Carlos Fernandez-Granda,Krzysztof J. Geras +22 more
TL;DR: The findings demonstrate the potential of the proposed system for assisting front-line physicians in the triage of COVID-19 patients and silently deployed a preliminary version of the deep neural network at New York University Langone Health during the first wave of the pandemic, which produced accurate predictions in real-time.
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The gravid uterus: MR imaging and reporting of abnormal placentation
TL;DR: MRI features and appropriate reporting ofplacenta previa and the placenta accreta spectrum are reviewed.