Statistics and behavior of clinically significant extra-pulmonary vein atrial fibrillation sources: machine-learning-enhanced electrographic flow mapping in persistent atrial fibrillation.

ABSTRACT:

Introduction

Electrographic flow (EGF) mapping is an FDA 510(k)-cleared method for visualizing atrial activation wavefronts in atrial fibrillation (AF). Its clinical efficacy was demonstrated in the FLOW-AF randomized controlled trial, and its fundamental principles have been previously described. However, the underlying machine learning strategy used to develop and refine the EGF algorithm has not yet been detailed. Here, we present how our EGF Model-trained on procedural outcomes from 199 fully anonymized retrospective patient datasets-identifies clinically significant sources of AF and how this machine learning-driven hyperparameter optimization underlies its clinical effectiveness. We also examine the statistical characteristics of the identified sources and their impact on cyc

SUBMITTER: Ruppersberg P 

PROVIDER: S-EPMC12417512 | biostudies-literature | 2025

REPOSITORIES: biostudies-literature