Atrial Fibrillation Treatment Stratification Based on Artificial Intelligence-Driven Analysis of the Electrophysiological Complexity.

ABSTRACT:

Background

Atrial Fibrillation (AF) treatment strategies are suboptimal and clinical predictors of success are limited. Artificial Intelligence (AI) has arisen as a powerful tool for treatment efficacy prediction.

Objective

We developed an AI-driven platform for the stratification of patients based on noninvasive Electrocardiographic Imaging (ECGI) biomarkers and clinical parameters to evaluate and predict optimal patient treatment.

Methods

We evaluated 204 patients treated according to clinical guidelines and characterized them at the electrophysiological level using ECGI recordings during AF. ECGI signals were calculated to obtain frequency and rotational biomarkers. Baseline clinical characteristics and treatment after inclusion were registered.

Results

A cl

SUBMITTER: de la Nava AMS 

PROVIDER: S-EPMC12337632 | biostudies-literature | 2025 Aug

REPOSITORIES: biostudies-literature