Key result
Machine learning using sinus rhythm signals localizes VT ablation targets with ~0.82 AUC in porcine models.
Why the study?
Recurrence of VT after ablation remains high due to difficulty locating critical sites, prompting evaluation of machine learning for identifying ablation targets from substrate maps.
Does a machine learning approach based on intracardiac electrogram features accurately localize ventricular tachycardia ablation targets in a porcine model of chronic myocardial infarction?
Does a machine learning approach based on intracardiac electrogram features accurately localize ventricular tachycardia ablation targets in a porcine model of chronic myocardial infarction?
Effect estimate: AUC 0.821
A machine learning approach using unipolar EGM features from sinus rhythm maps can accurately localize VT ablation targets in a preclinical chronic MI model.
Should not change clinical VT ablation practice; leaves open human validation of ML-guided mapping in chronic MI.
Aims: The recurrence rate of ventricular tachycardia (VT) after ablation remains high due to the difficulty in locating VT critical sites. This study proposes a machine learning approach for improved identification of ablation targets based on intracardiac electrograms (EGMs) features derived from standard substrate mapping in a chronic myocardial infarction (MI) porcine model. Methods and results: Thirteen pigs with chronic MI underwent invasive electrophysiological studies using multipolar catheters (Advisor™ HD grid, EnSite Precision™). Fifty-six substrate maps and 35 068 EGMs were collected during sinus rhythm and pacing from multiple sites, including left, right, and biventricular pacing. Ventricular tachycardia was induced in all pigs, and a total of 36 VTs were localized and mapped with early, mid-, and late diastolic components of the circuit. Mapping sites within 6 mm from these critical sites were considered as potential ablation targets. Forty-six signal features representing functional, spatial, spectral, and time-frequency properties were computed from each bipolar and unipolar EGM. Several machine learning models were developed to automatically localize ablation targets, and logistic regressions were used to investigate the association between signal features and VT critical sites. Random forest provided the best accuracy based on unipolar signals from sinus rhythm map, provided an area under the curve of 0.821 with sensitivity and specificity of 81.4% and 71.4%, respectively. Conclusion: This study demonstrates for the first time that machine learning approaches based on EGM features may support clinicians in localizing targets for VT ablation using substrate mapping. This could lead to the development of similar approaches in VT patients.
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Wang et al. (2025) studied Ventricular tachycardia in chronic myocardial infarction (n=13). Machine learning approach (Random forest) based on EGM features was evaluated on Accuracy of localizing VT ablation targets (AUC 0.821). A random forest machine learning model based on unipolar signals from sinus rhythm maps accurately localized VT ablation targets in a porcine model (AUC 0.821, sensitivity 81.4%, specificity 71.4%).
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