12 lead surface ECGs as a surrogate of atrial electrical remodeling - a deep learning based approach.

Background And Purpose: Atrial fibrillation (AF), a common arrhythmia, is linked with atrial electrical and structural changes, notably low voltage areas (LVAs) which are associated with poor ablation outcomes and increased thromboembolic risk. This study aims to evaluate the efficacy of a deep learning model applied to 12‑lead ECGs for non-invasively predicting the presence of LVAs, potentially guiding pre-ablation strategies and improving patient outcomes.

Methods: A retrospective analysis was conducted on 204 AF patients, who underwent catheter ablation.