Deep learning prediction of sex on chest radiographs: a potential contributor to biased algorithms.

Journal: Emergency radiology
Published Date: Jan 10, 2022

Abstract

BACKGROUND: Deep convolutional neural networks (DCNNs) for diagnosis of disease on chest radiographs (CXR) have been shown to be biased against males or females if the datasets used to train them have unbalanced sex representation. Prior work has suggested that DCNNs can predict sex on CXR, which could aid forensic evaluations, but also be a source of bias.

Authors

  • David Li
    Department of Preclinical Research, Angion Biomedica Corporation, Nassau, NY 11553, USA. david_li@college.harvard.edu.
  • Cheng Ting Lin
    The Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, Baltimore, MD, USA. clin97@jhmi.edu.
  • Jeremias Sulam
    Johns Hopkins University.
  • 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.

Keywords

External Resources

Popular Topics
Recent Journals