Toward Patient-Specific Prediction of Ablation Strategies for Atrial Fibrillation Using Deep Learning.

ABSTRACT: Atrial fibrillation (AF) is a common cardiac arrhythmia that affects 1% of the population worldwide and is associated with high levels of morbidity and mortality. Catheter ablation (CA) has become one of the first line treatments for AF, but its success rates are suboptimal, especially in the case of persistent AF. Computational approaches have shown promise in predicting the CA strategy using simulations of atrial models, as well as applying deep learning to atrial images. We propose a novel approach that combines image-based computational modelling of the atria with deep learning classifiers trained on patient-specific atrial models, which can be used to assist in CA therapy selection. Therefore, we trained a deep convolutional neural network (CNN) using a combination of (i) 122 atrial t

SUBMITTER: Muffoletto M 

PROVIDER: S-EPMC8187921 | biostudies-literature | 2021

REPOSITORIES: biostudies-literature