Application of Digital Twin Frameworks in Personalized Cardiac Electrophysiology Modeling to Predict Arrhythmia Risk and Optimize Ablation Therapy
Keywords:
Digital Twin, Cardiac Electrophysiology, Personalized Medicine, Arrhythmia Risk, Ablation Therapy, Computational Cardiology, Patient-Specific Modeling, Electrophysiology Simulation, Predictive Analytics, 3D Cardiac ModelingAbstract
Personalized cardiac electrophysiology modeling has witnessed growing attention in clinical and computational cardiology. With the emergence of Digital Twin (DT) technology, it is now feasible to integrate patient-specific data into dynamic models that can simulate individual cardiac behavior. This paper explores the use of DT frameworks in simulating electrophysiological phenomena to predict arrhythmia risk and enhance ablation therapy strategies. We contextualize this within 2019-era capabilities, surveying existing literature and identifying technological gaps that DT models help to address. Through architectural proposals, sequence diagrams, and visualized workflows, we present how a digital twin could be operationalized for cardiac ablation planning and real-time decision support.
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Copyright (c) -1 Fatema Kutaiba (Author)
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