Related Experiment Video

Updated: Aug 7, 2025

Estimating Bilateral Atrial Function by Cardiovascular Magnetic Resonance Feature Tracking in Patients with Paroxysmal Atrial Fibrillation
08:10

Estimating Bilateral Atrial Function by Cardiovascular Magnetic Resonance Feature Tracking in Patients with Paroxysmal Atrial Fibrillation

Published on: July 20, 2022

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An artificial intelligence-based model for prediction of atrial fibrillation from single-lead sinus rhythm

Tove Hygrell1, Fredrik Viberg1, Erik Dahlberg2

  • 1Department of Clinical Sciences, Karolinska Institutet, Danderyd University Hospital, Stockholm SE-182 88, Sweden.

Europace : European Pacing, Arrhythmias, and Cardiac Electrophysiology : Journal of the Working Groups on Cardiac Pacing, Arrhythmias, and Cardiac Cellular Electrophysiology of the European Society of Cardiology
|March 7, 2023
PubMed

Insights

An artificial intelligence network can predict atrial fibrillation (AF) from a normal sinus rhythm ECG. This AI approach shows improved accuracy with a wider patient age range for detecting paroxysmal AF.

Area of Science:

  • Cardiology
  • Artificial Intelligence
  • Medical Diagnostics

Background:

  • Screening for atrial fibrillation (AF) is recommended by guidelines, but its paroxysmal nature complicates detection.
  • Prolonged heart rhythm monitoring increases yield but is costly and inconvenient.

Purpose of the Study:

  • To evaluate the accuracy of an artificial intelligence (AI)-based network in predicting paroxysmal AF from a single-lead ECG showing normal sinus rhythm.

Main Methods:

  • A convolutional neural network was trained and validated on 478,963 single-lead ECGs from 14,831 patients aged 65+ years across three studies.
  • The AI model's predictive performance for paroxysmal AF was assessed using the area under the receiver operating characteristic curve (AUC).

Main Results:

  • The AI algorithm predicted paroxysmal AF from a single ECG with an AUC of 0.80 (CI 0.78-0.83) in a study with a wide age range (65-90+ years).
  • Performance was lower in age-homogenous groups (75-76 years), with AUCs of 0.62 in two separate studies.

Conclusions:

  • An AI-enabled network can predict AF from a sinus rhythm ECG.
  • The AI's predictive accuracy for AF is enhanced when applied to populations with a broader age distribution.
Abstract

Keywords:
Artificial intelligenceAtrial fibrillationIntermittent ECGScreening

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