Deep Learning to Quantify Pulmonary Edema in Chest Radiographs

202138 citationsJournal Articlegreen Open Access

Authors

Steven Horng · Beth Israel Deaconess Medical Center

Ruizhi Liao · Beth Israel Deaconess Medical Center

Xin Wang · Beth Israel Deaconess Medical Center

Sandeep Dalal · Beth Israel Deaconess Medical Center

Polina Golland · Beth Israel Deaconess Medical Center

Seth J. Berkowitz · Beth Israel Deaconess Medical Center

Abstract

Deep learning models were trained on a large chest radiograph dataset and could grade the severity of pulmonary edema on chest radiographs with high performance.<i>Supplemental material is available for this article.</i>See also the commentary by Auffermann in this issue.© RSNA, 2021.

Topics & Keywords

COVID-19 diagnosis using AI Phonocardiography and Auscultation Techniques Lung Cancer Diagnosis and Treatment

Publication Details

Published in: Radiology Artificial Intelligence

Volume 3, Issue 2, pp. e190228-e190228

DOI: 10.1148/ryai.2021190228

Field-Weighted Citation Impact: 4.30

Command Palette

Deep Learning to Quantify Pulmonary Edema in Chest Radiographs

Authors

Abstract

Topics & Keywords

Publication Details