Key result
A digital twin framework for virtual-heart arrhythmia ablation targeting reproduced VT morphologies in 15 of 15 cases and reduced execution time by 90%.
Why the study?
Existing virtual-heart arrhythmia ablation targeting methods are computationally intensive and often lack validation against clinical VT and ECG data.
Does an optimized VAAT pipeline accurately predict VT inducibility and morphology compared to in vivo data in swine models of post-MI VT?
Does an optimized VAAT pipeline accurately predict VT inducibility and morphology compared to in vivo data in swine models of post-MI VT?
An optimized digital twin framework accurately predicts post-MI VT inducibility and morphology in swine models with significantly reduced computational time, supporting its potential for real-time intraprocedural ablation guidance.
Answers are grounded in this paper and cite their sources.
Feasibility shown for real-time VT ablation guidance in swine; leaves open human translation and prospective validation.
BACKGROUND: Postinfarction ventricular tachycardia (VT) arises from reentrant circuits within partially conductive myocardial tissue. Despite antiarrhythmic medication, many patients require ablation therapy, yet recurrence rates remain as high as 50% at 5 years. Virtual-heart arrhythmia ablation targeting (VAAT) methods aim to improve outcomes by simulating VT activity preoperatively, but they are computationally intensive and often lack validation against clinical VT and electrocardiographic (ECG) data. OBJECTIVE: This study aimed to optimize a VAAT pipeline for speed and clinical relevance, enabling real-time use during ablation and validating its predictive accuracy against experimental VT and ECG data. METHODS: We developed a digital twin framework incorporating semiautomatic anatomic model generation, a simplified Mitchell-Schaeffer action potential model, a graphics processing unit-accelerated lattice-Boltzmann solver for the monodomain equation, fast ECG computation via the boundary element method, and semiautomated VT inducibility analysis. The framework was applied to 9 swine models of postmyocardial infarction VT across 3 stages: before ablation, after ablation, and 1-week follow-up. RESULTS: The digital twin predicted VT inducibility in all clinically inducible cases and reproduced VT morphologies with polarity match in ≥11 of 12 ECG leads in 15 of 15 cases (12 by inducibility, 3 by pace mapping). It also identified 5 nonclinical VTs not inducible in vivo, suggesting its ability to uncover potential future VTs. A reduced parameter set enabled simulation of all unique VT morphologies while reducing execution time by 90%, supporting feasibility for intraoperative use. CONCLUSION: The optimized VAAT pipeline achieved robust predictive performance and significant computational gains, potentially enabling real-time assessment of ablation completeness and enhanced procedural guidance.
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Castañeda et al. (2026) studied Postmyocardial infarction ventricular tachycardia (n=9). Virtual-heart arrhythmia ablation targeting (VAAT) digital twin framework was evaluated on Prediction of VT inducibility and reproduction of VT morphologies (polarity match in ≥11 of 12 ECG leads). A digital twin framework for virtual-heart arrhythmia ablation targeting reproduced VT morphologies in 15 of 15 cases and reduced execution time by 90%.
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