Determination of disease severity in COVID-19 patients using deep learning in chest X-ray images | Zendy

Maxime Blain | Zendy; Michael T. Kassin | Zendy; Nicole Varble | Zendy; Xiaosong Wang | Zendy; Ziyue Xu | Zendy; Daguang Xu | Zendy; Gianpaolo Carrafiello | Zendy; Valentina Vespro | Zendy; Elvira Stellato | Zendy; Anna Maria Ierardi | Zendy; Letizia Di Meglio | Zendy; Robert D. Suh | Zendy; Stephanie A. Walker | Zendy; Sheng Xu | Zendy; Thomas Sanford | Zendy; Evrim Türkbey | Zendy; Stephanie A. Harmon | Zendy; Barış Türkbey | Zendy; Bradford J. Wood | Zendy

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Determination of disease severity in COVID-19 patients using deep learning in chest X-ray images

Author(s) -

Maxime Blain,

Michael T. Kassin,

Nicole Varble,

Xiaosong Wang,

Ziyue Xu,

Daguang Xu,

Gianpaolo Carrafiello,

Valentina Vespro,

Elvira Stellato,

Anna Maria Ierardi,

Letizia Di Meglio,

Robert D. Suh,

Stephanie A. Walker,

Sheng Xu,

Thomas Sanford,

Evrim Türkbey,

Stephanie A. Harmon,

Barış Türkbey,

Bradford J. Wood

Publication year - 2020

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 , covid-19 , radiology , disease , radiography , pathology , infectious disease (medical specialty) , outbreak

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.

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