Open AccessDetermination of disease severity in COVID-19 patients using deep learning in chest X-ray images
Author(s) -
Maxime Blain,
Michael Kassin,
Nicole Varble,
Xiaosong Wang,
Ziyue Xu,
Daguang Xu,
Gianpaolo Carrafiello,
Valentina Vespro,
Elvira Stellato,
Letizia Di Meglio,
Robert D. Suh,
Stephanie A. Walker,
Sheng Xu,
Thomas Sanford,
Evrim Türkbey,
Stephanie Harmon,
Barış Türkbey,
Bradford J. Wood
Publication year - 2021
Publication title -
diagnostic and interventional radiology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.754
H-Index - 43
eISSN - 1305-3612
pISSN - 1305-3825
DOI - 10.5152/dir.2020.20205
Subject(s) - medicine , concordance , radiography , interstitial lung disease , radiology , covid-19 , lung , retrospective cohort study , disease , infectious disease (medical specialty)
Chest X-ray plays a key role in diagnosis and management of COVID-19 patients and imaging features associated with clinical elements may assist with the development or validation of automated image analysis tools. We aimed to identify associations between clinical and radiographic features as well as to assess the feasibility of deep learning applied to chest X-rays in the setting of an acute COVID-19 outbreak.