Key result
Transformer-based DL model achieves 0.915 AUC for localizing PVC origins in pre-ablation planning.
Why the study?
Traditional 12-lead ECG interpretation to localize premature ventricular contractions for catheter ablation requires expert input and can be inconsistent, leaving a need for reproducible deep learning methods.
Does a transformer-based deep learning model using standard 12-lead ECGs accurately localize the origin of idiopathic PVCs compared to ablation-confirmed origins?
Does a transformer-based deep learning model using standard 12-lead ECGs accurately localize the origin of idiopathic PVCs compared to ablation-confirmed origins?
A transformer-based deep learning model can accurately localize idiopathic PVC origins from standard 12-lead ECGs, offering a reliable non-invasive tool for pre-ablation planning.
Answers are grounded in this paper and cite their sources.
May aid pre-ablation planning; hypothesis-generating pending prospective validation.
Background Accurate localization of premature ventricular contractions (PVCs) is critical for successful catheter ablation, particularly for origins in the ventricular outflow tract (OT) or other sites. Traditional 12-lead ECG interpretation often requires expert input and may yield inconsistent results. Transformer-based deep learning (DL) offers a reproducible, non-invasive solution using ECG data alone. Objective To develop and validate a transformer-based DL model using 12-lead ECGs to localize PVC origins into four anatomical categories: left ventricular outflow tract (LVOT), right ventricular outflow tract (RVOT), left ventricular non-OT (LV Non-OT), and right ventricular non-OT (RV Non-OT). The model performs two binary tasks: (1) OT vs. non-OT and (2) left vs. right ventricular origin. Methods This retrospective study included 584 patients (mean age 46.5 ± 14.3 years; 255 men, 329 women) who underwent catheter ablation for idiopathic PVCs. After ECG quality review, 470 standard 10-second 12-lead ECGs were selected. Two transformer-based models were independently trained using patient-level splits (70% training, 10% validation, 20% test). Final localization was derived by combining output probabilities. Model performance was evaluated against ablation-confirmed PVC origins (Figure 1). Results PVC origins were classified as follows: RVOT (295, 50.5%), LVOT (93, 15.9%), anterolateral mitral annulus (37, 6.3%), posteromedial papillary muscle (21, 3.6%), RV inflow (16, 2.7%), LV summit (14, 2.4%), anterolateral papillary muscle (13, 2.2%), and others (95, 16.3%). The model achieved AUCs of 0.98 for LVOT, 0.91 for RVOT, 0.84 for LV Non-OT, and 0.93 for RV Non-OT, with a macro-average AUC of 0.915 (Figure 2). Performance remained consistent across internal validation, supporting its clinical utility. Conclusion This study presents a clinically validated transformer-based DL model capable of accurately localizing the origin of PVCs using standard 12-lead ECGs. The model consistently achieved high diagnostic performance and may serve as a valuable non-invasive tool to support pre-ablation planning in patients with ventricular arrhythmias
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Baek et al. (2026) studied this question. The transformer-based DL model achieved macro-average AUC of 0.915 for localizing PVC origins, supporting its clinical utility in pre-ablation planning.
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