Demographic Reporting in Publicly Available Chest Radiograph Data Sets: Opportunities for Mitigating Sex and Racial Disparities in Deep Learning Models.

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

Abstract

OBJECTIVE: Data sets with demographic imbalances can introduce bias in deep learning models and potentially amplify existing health disparities. We evaluated the reporting of demographics and potential biases in publicly available chest radiograph (CXR) data sets.

Authors

  • Paul H Yi
    The Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, Baltimore, Maryland. Electronic address: Pyi10@jhmi.edu.
  • Tae Kyung Kim
    The Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, 601 N. Caroline St., Room 4223, Baltimore, MD, 21287, USA.
  • Eliot Siegel
    University of Maryland School of Medicine, Department of Diagnostic Radiology and Nuclear Medicine, 504 E. Fort Ave Baltimore, MD 21230.
  • Noushin Yahyavi-Firouz-Abadi
    The Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, Baltimore, Maryland. Electronic address: nyahyav1@jhmi.edu.

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