Exploring interpretability in deep learning prediction of successful ablation therapy for atrial fibrillation.

ABSTRACT: Background: Radiofrequency catheter ablation (RFCA) therapy is the first-line treatment for atrial fibrillation (AF), the most common type of cardiac arrhythmia globally. However, the procedure currently has low success rates in dealing with persistent AF, with a reoccurrence rate of ∼50% post-ablation. Therefore, deep learning (DL) has increasingly been applied to improve RFCA treatment for AF. However, for a clinician to trust the prediction of a DL model, its decision process needs to be interpretable and have biomedical relevance. Aim: This study explores interpretability in DL prediction of successful RFCA therapy for AF and evaluates if pro-arrhythmogenic regions in the left atrium (LA) were used in its decision process. Methods: AF and its t

SUBMITTER: Ogbomo-Harmitt S 

PROVIDER: S-EPMC10043207 | biostudies-literature | 2023

REPOSITORIES: biostudies-literature