End-to-end Risk Prediction of Atrial Fibrillation from the 12-Lead ECG by Deep Neural Networks

TL;DR

This paper presents a deep neural network model that predicts future atrial fibrillation risk from 12-lead ECGs with high accuracy, aiding clinical decision-making and patient management.

Contribution

The study develops and validates a novel deep learning model for AF risk prediction using a large Brazilian ECG dataset, demonstrating its effectiveness.

Findings

AUC score of 0.845 for predicting future AF

High-risk patients are 50% more likely to develop AF within 40 weeks

Low-risk patients have over 85% chance of remaining AF free for seven years

Abstract

Background: Atrial fibrillation (AF) is one of the most common cardiac arrhythmias that affects millions of people each year worldwide and it is closely linked to increased risk of cardiovascular diseases such as stroke and heart failure. Machine learning methods have shown promising results in evaluating the risk of developing atrial fibrillation from the electrocardiogram. We aim to develop and evaluate one such algorithm on a large CODE dataset collected in Brazil. Results: The deep neural network model identified patients without indication of AF in the presented ECG but who will develop AF in the future with an AUC score of 0.845. From our survival model, we obtain that patients in the high-risk group (i.e. with the probability of a future AF case being greater than 0.7) are 50% more likely to develop AF within 40 weeks, while patients belonging to the minimal-risk group (i.e. with the probability of a future AF case being less than or equal to 0.1) have more than 85% chance of remaining AF free up until after seven years. Conclusion: We developed and validated a model for AF risk prediction. If applied in clinical practice, the model possesses the potential of providing valuable and useful information in decision-making and patient management processes.