End-to-end Risk Prediction of Atrial Fibrillation from the 12-Lead ECG
  by Deep Neural Networks
Theogene Habineza Antônio H. Ribeiro Daniel Gedon Joachim A. Behar Antonio Luiz P. Ribeiro Thomas B. Schön
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.