Why the study?
Concerns regarding practical implementation and prediction robustness due to imaging uncertainty have hindered clinical translation of personalized digital twin guidance for VT catheter ablation.
Does a computational model incorporating scar uncertainty improve the identification of VT ablation targets in patients with structural heart disease?
Population
Ischemic ablation patients
Comparison
Simulated VT circuit ECG fingerprints vs clinical recordings
Design
Computational modeling and validation study
Key result
An uncertainty-aware computational modeling framework successfully predicted clinical ventricular tachycardia circuits, achieving a mean ECG correlation coefficient of 0.845 with clinical recordings in ischemic patients.
Authors
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New framework improves robustness of digital twin VT models; leaves open whether it improves ablation outcomes in practice.
Observational (n=14)
No
Does a computational model incorporating scar uncertainty improve the identification of VT ablation targets in patients with structural heart disease?
Incorporating uncertainty in CT-derived scar reconstruction into personalized computational models allows for near-real-time, accurate prediction of VT ablation targets that closely match clinical ECGs and mapping data.
Qadri et al. (2026) conducted an observational in Ventricular tachycardia (n=14). Uncertainty-aware computational modeling (SATHe Map) vs. Clinical electro-anatomical mapping and ECG recordings was evaluated on Correlation coefficient between simulated and clinical 12-lead ECG signatures of VT circuits. An uncertainty-aware computational modeling framework successfully predicted clinical ventricular tachycardia circuits, achieving a mean ECG correlation coefficient of 0.845 with clinical recordings in ischemic patients.
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