aceLarge Language Model-Based Localization of Premature Ventricular Contraction Origins: A Retrospective Diagnostic Accuracy Study
Background Accurate localization of premature ventricular contraction (PVC) origin from 12-lead electrocardiography (ECG) is important for procedural planning in catheter ablation. Although convolutional neural network (CNN)-based models have shown promising diagnostic performance, they require task-specific training and remain limited in interpretability. We evaluated whether large language model (LLM)-based ECG image interpretation could perform binary left-versus-right PVC origin localization from 12-lead ECG…
LLM-based staged extraction framework localized PVC origin as left versus right from 12-lead ECG images with discrimination comparable to a CNN baseline while providing a traceable stepwise diagnostic process.
High-PPV operating point was derived from training data and requires prospective external validation before clinical use.
Evidence
- Peer-reviewedJournal of Cardiovascular Electrophysiology2026-08-01
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Truvace Impact Record TRV-2026-0629, v1: “Large Language Model-Based Localization of Premature Ventricular Contraction Origins: A Retrospective Diagnostic Accuracy Study.” Truvace, 2026-08-03. /record/TRV-2026-0629 (accessed at citation time). sha256 5c1ae225c69af2c2…
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