Innovating Cardiac Ablation: Integrative Artificial Intelligence Approaches for Personalized Arrhythmia Management-Innovating Cardiac Ablation: Integrative Artificial Intelligence Approaches for Personalized Arrhythmia Management

Innovating Cardiac Ablation: Integrative Artificial Intelligence Approaches for Personalized Arrhythmia Management

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At the Heart Rhythm Society 2025 meeting in San Diego, a session featuring collaborations between HRS and APHRS highlighted the progress in atrial fibrillation treatment and the integration of artificial intelligence (AI). Dr. Yen-Jung Lin from Taiwan discussed AI's potential in improving persistent atrial fibrillation ablation beyond pulmonary vein isolation (PVI). He emphasized that while PVI remains a fundamental therapy, its effectiveness diminishes in persistent cases. AI can enhance treatment by identifying atrial fibrillation drivers.<br /><br />Dr. Natalia Trayanova from Johns Hopkins presented on integrating AI with clinical practice through digital twins. These models mimic real cardiac behavior and predict outcomes, assisting in procedures like ventricular tachycardia ablation. Trayanova noted a digital twin must not only predict ablation targets but also simulate post-ablation scenarios to inform effective decision-making.<br /><br />Dr. Patrick Boyle from the University of Washington discussed using AI to enhance pre-procedure planning. His focus was on using explainer AI to understand arrhythmia mechanisms, predicting the likelihood of recurrence post-ablation. Boyle's work with machine learning and hybrid computational modeling also explored substrate complexity, considering fibrosis and other contributors to arrhythmogenicity.<br /><br />Together, these presentations underscored AI's growing role in personalizing and improving arrhythmia treatment, and emphasized ongoing research to better integrate these technologies into clinical settings.

Keywords

Heart Rhythm Society 2025

atrial fibrillation treatment

artificial intelligence

pulmonary vein isolation

digital twins

ventricular tachycardia ablation

pre-procedure planning

arrhythmia mechanisms

machine learning

hybrid computational modeling