Detecting Racial/Ethnic Health Disparities Using Deep Learning From Frontal Chest Radiography.

Journal: Journal of the American College of Radiology : JACR
Published Date: Jan 1, 2022

Abstract

PURPOSE: The aim of this study was to assess racial/ethnic and socioeconomic disparities in the difference between atherosclerotic vascular disease prevalence measured by a multitask convolutional neural network (CNN) deep learning model using frontal chest radiographs (CXRs) and the prevalence reflected by administrative hierarchical condition category codes in two cohorts of patients with coronavirus disease 2019 (COVID-19).

Authors

  • Ayis Pyrros
    DuPage Medical Group, Radiology. Electronic address: ayis@ayis.org.
  • Jorge Mario Rodríguez-Fernández
    University of Illinois at Chicago, Department of Neurology.
  • Stephen M Borstelmann
    University of Central Florida School of Medicine, UCF College of Medicine, 6850 Lake Nona Blvd, Orlando, FL 32827. Electronic address: sborstelmannmd@gmail.com.
  • Judy Wawira Gichoya
    Department of Interventional Radiology, Oregon Health & Science University, Portland, Oregon; Department of Radiology and Imaging Sciences, Emory University, Atlanta, Georgia.
  • Jeanne M Horowitz
    Department of Radiology, Northwestern Memorial Hospital, Northwestern University, Chicago, Illinois.
  • Brian Fornelli
    Anthem Inc, Chicago, Illinois.
  • Nasir Siddiqui
    DuPage Medical Group, Radiology.
  • Yury Velichko
    Department of Radiology, Feinberg School of Medicine, Northwestern University, 737 N. Michigan Ave, Suite 1600, Chicago, IL, 60611, USA.
  • Oluwasanmi Koyejo Sanmi
    Department of Computer Science, University of Illinois at Urbana-Champaign, Urbana-Champaign, Illinois.
  • William Galanter
    Department of Medicine, College of Medicine, University of Illinois at Chicago, Chicago, Illinois, United States of America.

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