Digital Twin Models in Atrial Fibrillation: Charting the Future of Precision Therapy?

Abstract

Atrial fibrillation (AF) is the most common sustained arrhythmia and a major contributor to stroke and cardiovascular morbidity. However, current approaches to rhythm control and stroke prevention are often limited by variable treatment responses and population-based risk stratification tools that fail to capture individual disease mechanisms. Digital twin technology-computational models built using patient-specific anatomical and physiological data-has emerged as a promising approach to address these limitations. In the context of AF, left atrial (LA) digital twins integrate structural, electrophysiological, and hemodynamic information to simulate arrhythmia behavior, therapeutic response, and thromboembolic risk with high mechanistic fidelity. Recent applications include stroke risk prediction using computational fluid dynamics, in silico testing of antiarrhythmic drugs, and virtual planning of catheter ablation strategies. These models have shown potential to enhance the personalization of care, offering a more nuanced and predictive framework than conventional scoring systems or imaging alone. Despite promising progress, challenges related to model personalization, computational scalability, and clinical validation remain. Nevertheless, LA digital twins are poised to advance the precision management of AF by bridging in silico modeling with real-world decision-making. This review summarizes the current state and future directions of left atrial digital twin models in AF, focusing on their application in stroke risk prediction, pharmacologic decision-making, and ablation strategy optimization.