Assessing and Mitigating Bias in Medical Artificial Intelligence: The Effects of Race and Ethnicity on a Deep Learning Model for ECG Analysis.

Journal: Circulation. Arrhythmia and electrophysiology
Published Date: Mar 1, 2020

Abstract

BACKGROUND: Deep learning algorithms derived in homogeneous populations may be poorly generalizable and have the potential to reflect, perpetuate, and even exacerbate racial/ethnic disparities in health and health care. In this study, we aimed to (1) assess whether the performance of a deep learning algorithm designed to detect low left ventricular ejection fraction using the 12-lead ECG varies by race/ethnicity and to (2) determine whether its performance is determined by the derivation population or by racial variation in the ECG.

Authors

  • Peter A Noseworthy
    Department of Cardiovascular Medicine, Mayo Clinic, Rochester, Minnesota.
  • Zachi I Attia
    Department of Cardiovascular Medicine, Mayo Clinic College of Medicine, Rochester, Minnesota, USA.
  • LaPrincess C Brewer
    Department of Cardiovascular Medicine (P.A.N., Z.I.A., L.C.B., S.N.H., X.Y., S.K., P.A.F., F.L.-J.), Mayo Clinic, Rochester, MN.
  • Sharonne N Hayes
    Department of Cardiovascular Medicine (P.A.N., Z.I.A., L.C.B., S.N.H., X.Y., S.K., P.A.F., F.L.-J.), Mayo Clinic, Rochester, MN.
  • Xiaoxi Yao
    Department of Health Sciences Research, Division of Health Care Policy and Research, Mayo Clinic, Rochester, Minnesota.
  • Suraj Kapa
    Cardiovascular Medicine, Mayo Clinic, Rochester, MN, USA.
  • Paul A Friedman
    Department of Cardiovascular Medicine, Mayo Clinic College of Medicine, Rochester, Minnesota, USA.
  • Francisco Lopez-Jimenez
    Department of Cardiovascular Medicine, Mayo Clinic, Rochester, Minnesota, USA.

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