Multimodal Characterization of Atrial Fibrillation: From Patient-Specific Anatomy and Electrophysiology to...

This narrative review synthesizes a comprehensive multimodal characterization framework for atrial fibrillation (AF), tracing its progression from patient-specific anatomical reconstruction to electrophysiological phenotyping and standardized spatial mapping. The literature was identified through searches of PubMed, Web of Science, and Google Scholar, covering publications from January 2010 to June 2026, with additional studies identified from relevant references. Anatomical characterization leverages clinical imaging modalities alongside advanced deep learning models to execute precise whole-chamber, subregional, and tissue-level modeling. Electrophysiological profiling spans multi-scale modalities, including 12-lead electrocardiography (ECG), body surface potential mapping (BSPM), electrocardiographic imaging (ECGI), and electroanatomical mapping (EAM), complemented by wearable sensors for longitudinal rhythm surveillance. To bridge heterogeneous datasets across subjects and modalities, standardized coordinate systems and multimodal registration enable reproducible data integration. These integrated data parameterize patient-specific computational models to advance mechanistic insight, risk stratification, and personalized AF management. However, broad clinical translation remains constrained by imaging variability, reconstruction noise, registration uncertainty, and limited prospective multicenter validation. Future progress hinges on moving beyond technical accuracy toward prospective trials evaluating algorithm-guided clinical utility.