Artificial Intelligence and Signal Processing Technologies for Atrial Fibrillation Mapping and Ablation

Atrial fibrillation (AF) represents a complex disorder of cardiac rhythm, deeply rooted in the intricate interplay between molecular, cellular, and organ-level networks. Recent advances have underscored the importance of understanding AF not only as a local electrical disturbance but as an emergent property of multi-scale physiological networks encompassing electrophysiological, structural, autonomic, and inter-organ connections. Despite growing insights, AF remains a prominent cause of morbidity due to its association with increased risks of stroke and heart failure, and the mechanisms driving its initiation, maintenance, and recurrence continue to spark debate. Studies highlight that technological innovation—spanning signal processing, artificial intelligence, computational modeling and simulation, and novel energy modalities like pulse field ablation—has dramatically impacted catheter-based therapy and advanced our capacity to map and modify complex arrhythmic networks. However, the integration of these technologies within the framework of network physiology, and their potential to reshape personalized and systems-level ablation strategies, remains incompletely explored.This Research Topic aims to illuminate the intersection of network physiology and technological advancement in the context of atrial fibrillation (AF) ablation. We seek to unravel how emerging computational and analytical methods can deepen our understanding of AF dynamics and translate these insights into innovative therapeutic approaches. Central objectives include elucidating the impact of complex signal analysis and AI-driven mapping or ablation, identification and targeting of sites beyond pulmonary veins, exploring digital twin and simulation platforms for personalized intervention, and evaluating how AF atrium respond to diverse energy sources and ablation modalities.To gather further insights into the network physiological boundaries and implications of AF ablation technologies, we welcome articles addressing, but not limited to, the following themes:• Multi-scale network analysis of electrophysiological mechanisms in AF• Integration of AI and advanced signal processing to decode arrhythmic networks• Digital twins and computational simulations for personalized ablation strategies• Effects of novel energy sources (e.g., pulse field ablation) on cardiac dynamics• Interplay between cardiac, autonomic, and systemic physiological networks during AF and its effect on ablation • Approaches for real-time intervention and monitoring in clinical and preclinical settingsDr. Prasanth Ganesan is an inventor on patents owned by Florida Atlantic University. All the other Topic Editors declare no conflicts of interest. Topic editors: Prasanth Ganesan, Nicolai Spicher, Abouzar Kaboudian Source: https://www.frontiersin.org/research-topics/79276/artificial-intelligence-and-signal-processing-technologies-for-atrial-fibrillation-mapping-and-ablation