Efficient Generation of Cardiac Digital Twins for Personalized Atrial Fibrillation Treatment Using Non-Invasive ECGI Data

Atrial Fibrillation (AF) is a prevalent cardiac arrythmia.AF poses significant challenges to patient wellbeing and longevity as current treatments are not effective and personalized as desired.While cardiac modelling has advanced considerably, the translation of these models into personalized therapies remains challenging.This paper proposes a framework leveraging Digital Twins (DTs) based on non-invasive electrocardiographic imaging (ECGI) data to enhance the planning and efficacy of ablation procedures.The framework was validated against benchmark simulations across diverse AF scenarios utilizing a cellular automata model.DTs are personalized using ECGI-derived metrics, as Dominant Frequency (DF) maps, to modify tissue properties and simulate arrhythmia dynamics.The DF comparison between benchmark simulations and DTs shows a MAE of 1.15 Hz.Additionally, the ablation strategy effective in stopping arrhythmia in benchmark simulations also works in DTs.The framework efficiently generates DTs that replicate benchmark dynamics using non-invasive data, with DF maps playing a crucial role in DT personalization.With its reliance on non-invasive data and efficient computational methods, the framework shows potential for real-time clinical use, assisting personalized AF ablation strategies.