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

Journal: Diagnostic and interventional radiology (Ankara, Turkey)
Published Date:

Abstract

PURPOSE: 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.

Authors

  • Maxime Blain
    Department of Radiology, Hopital Henri Mondor, AP-HP, 94000 Créteil, France.
  • Michael T Kassin
    Center for Interventional Oncology, National Institutes of Health Clinical Center and National Cancer Institute, Bethesda, Maryland, USA.
  • Nicole Varble
    Center for Interventional Oncology, Radiology and Imaging Sciences, NIH Clinical Center and National Cancer Institute, Center for Cancer Research, National Institutes of Health, Bethesda, MD, USA.
  • Xiaosong Wang
    Imaging Biomarkers and Computer-aided Diagnosis Lab, Radiology and Imaging Sciences, NIH Clinical Center, Bethesda, MD, 20892-1182, USA.
  • Ziyue Xu
    Center for Infectious Disease Imaging (CIDI), Radiology and Imaging Science Department, National Institutes of Health (NIH), Bethesda, MD 20892, United States.
  • Daguang Xu
    NVIDIA, Santa Clara, CA, USA.
  • Gianpaolo Carrafiello
    Radiology Department, Foundation IRCCS Cà Granda-Ospedale Maggiore Policlinico, Milan, Italy.
  • Valentina Vespro
    Department of Radiology,Fondazione IRCCS Cà Granda Ospedale Maggiore Policlinico, University of Milan, Italy.
  • Elvira Stellato
    Department of Radiology Fondazione IRCCS Cà Granda, Ospedale Maggiore Policlinico Milano, Milan, Italy.
  • Anna Maria Ierardi
    Radiology Department, Foundation IRCCS Cà Granda-Ospedale Maggiore Policlinico, Milan, Italy.
  • Letizia Di Meglio
    Department of Radiology,Fondazione IRCCS Cà Granda Ospedale Maggiore Policlinico, University of Milan, Italy.
  • Robert D Suh
    Department of Radiology, University of California Los Angeles, Los Angeles, California, USA.
  • Stephanie A Walker
    National Cancer Institute, National Institutes of Health, Bethesda, Maryland, USA.
  • Sheng Xu
    School of Physics and Information Engineering, Jiangsu Second Normal University, Nanjing, 211200, China.
  • Thomas H Sanford
    Center for Interventional Oncology, National Institutes of Health Clinical Center and National Cancer Institute, Bethesda, Maryland, USA;State University of New York Upstate Medical University, Syracuse, Newyork, USA.
  • Evrim B Turkbey
    Molecular Imaging Program, National Institutes of Health, Bethesda, Maryland, USA;Department of Radiology and Imaging Sciences, National Institutes of Health, Bethesda, Mayland, USA.
  • Stephanie Harmon
  • Baris Turkbey
    Molecular Imaging Program, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA.
  • Bradford J Wood
    Center for Interventional Oncology, National Institutes of Health Clinical Center and National Cancer Institute, Bethesda, Maryland, USA;Department of Radiology, University of California Los Angeles, Los Angeles, California, USA;Department of Radiology and Imaging Sciences, National Institutes of Health, Bethesda, Mayland, USA.