Artificial Intelligence-Enabled ECG to Identify Silent Atrial Fibrillation in Embolic Stroke of Unknown Source.

Journal: Journal of stroke and cerebrovascular diseases : the official journal of National Stroke Association
PMID: 34303963

Abstract

OBJECTIVES: Embolic strokes of unknown source (ESUS) are common and often suspected to be caused by unrecognized paroxysmal atrial fibrillation (AF). An AI-enabled ECG (AI-ECG) during sinus rhythm has been shown to identify patients with unrecognized AF. We pursued this study to determine if the AI-ECG model differentiates between patients with ESUS and those with known causes of stroke, and to evaluate whether the AF prediction by AI-ECG among patients with ESUS was associated with the results of prolonged ambulatory cardiac rhythm monitoring.

Authors

  • Alejandro A Rabinstein
    Department of Neurology, Mayo Clinic, Rochester, MN, USA.
  • Micah D Yost
    Neurology, Mayo Clinic, 200 First Street SW, Mayo W8B, Rochester, MN 55905, USA. Electronic address: Yost.Micah@mayo.edu.
  • Louis Faust
    Health Science Research, Mayo Clinic, Rochester, MN 55905, USA; Internal Medicine, Mayo Clinic, Rochester, MN 55905, USA. Electronic address: Faust.Louis@mayo.edu.
  • Anthony H Kashou
    Department of Medicine, Mayo Clinic, Rochester, Minnesota.
  • Omar S Latif
    Internal Medicine, Mayo Clinic, Rochester, MN 55905, USA. Electronic address: Latif.Omar@mayo.edu.
  • Jonathan Graff-Radford
    Department of Neurology, Mayo Clinic and Foundation, Rochester, MN, USA.
  • Itzhak Zachi Attia
    Department of Cardiovascular Medicine, Mayo Clinic, Rochester, MN, USA.
  • Xiaoxi Yao
    Department of Health Sciences Research, Division of Health Care Policy and Research, Mayo Clinic, Rochester, Minnesota.
  • Peter A Noseworthy
    Department of Cardiovascular Medicine, Mayo Clinic, Rochester, Minnesota.
  • Paul A Friedman
    Department of Cardiovascular Medicine, Mayo Clinic College of Medicine, Rochester, Minnesota, USA.

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