Key result
AI improves PVC origin localization accuracy and efficiency by extracting high-dimensional ECG features.
Why the study?
Traditional manual ECG analysis for locating PVC origin sites is highly subjective and inefficient, failing to meet clinical precision treatment demands.
Do artificial intelligence algorithms improve the accuracy of premature ventricular contraction origin localization compared to traditional manual ECG criteria?
Do artificial intelligence algorithms improve the accuracy of premature ventricular contraction origin localization compared to traditional manual ECG criteria?
AI models show significant promise for highly accurate, automated localization of PVC origins from 12-lead ECGs, but require rigorous multicenter validation and improved interpretability before routine clinical adoption.
AI may enable objective PVC origin localization for ablation; leaves open prospective validation before clinical use.
Premature ventricular contraction (PVC) is one of the most common types of arrhythmias, and accurately locating the origin sites of PVC is the key to establishing catheter ablation strategies. However, traditional manual analysis methods that rely on electrocardiogram (ECG) features are highly subjective and inefficient, making it difficult to meet the demands of clinical precision treatment. The emergence of artificial intelligence (AI) has provided new opportunities to address these limitations through automated analysis of complex ECG data. This review traces the evolution of ECG-based PVC localization criteria, summarizes recent advances in AI algorithm models for identifying PVC origins. Recent studies have shown that AI-based approaches can improve the efficiency and accuracy of PVC origin identification by extracting high-dimensional features from ECG data and enabling automated classification of different origin sites. This review discusses the prospects and challenges of AI application in the precise diagnosis of PVC.
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Wang et al. (2026) conducted a review in Premature ventricular contraction. Artificial intelligence (AI) algorithms vs. Traditional manual ECG analysis was evaluated. Artificial intelligence-based approaches improve the efficiency and accuracy of premature ventricular contraction origin identification by extracting high-dimensional features from ECG data.
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