Artificial intelligence for the detection, prediction, and management of atrial fibrillation.

Journal: Herzschrittmachertherapie & Elektrophysiologie
Published Date:

Abstract

The present article reviews the state of the art of machine learning algorithms for the detection, prediction, and management of atrial fibrillation (AF), as well as of the development and evaluation of artificial intelligence (AI) in cardiology and beyond. Today, AI detects AF with a high accuracy using 12-lead or single-lead electrocardiograms or photoplethysmography. The prediction of paroxysmal or future AF currently operates at a level of precision that is too low for clinical use. Further studies are needed to determine whether patient selection for interventions may be possible with machine learning.

Authors

  • Jonas L Isaksen
    University of Copenhagen, 2200, Copenhagen N, Denmark.
  • Mathias Baumert
    School of Electrical and Electronic Engineering, The University of Adelaide, Adelaide, SA, Australia.
  • Astrid N L Hermans
    Department of Cardiology, Maastricht University Medical Center and Cardiovascular Research Institute Maastricht, Maastricht, The Netherlands.
  • Molly Maleckar
    Department of Computational Physiology, Simula Research Laboratory, Oslo, Norway.
  • Dominik Linz
    Department of Biomedical Sciences, University of Copenhagen, Copenhagen, Denmark. dominik.linz@gmx.de.