Advancements in artificial intelligence for the localization of premature ventricular contraction origins

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.