Key result
AI-based ECG analysis detects ventricular arrhythmias with ~96% sensitivity and high overall accuracy.
Why the study?
Traditional LVEF-based risk stratification for ventricular arrhythmias has limited sensitivity, and although AI-based ECG analysis has emerged as a potential tool for automated detection, reported performance varies across studies.
Does artificial intelligence-based ECG analysis accurately detect ventricular arrhythmias in ECG segments?
Meta-Analysis
Does artificial intelligence-based ECG analysis accurately detect ventricular arrhythmias in ECG segments?
AI-based ECG algorithms demonstrate high diagnostic accuracy (96.4% sensitivity, 99.0% specificity) for detecting ventricular arrhythmias across diverse model architectures.
Supports AI ECG tools for ventricular arrhythmia detection; extends validation across algorithms in large datasets.
Background Ventricular arrhythmias (VA), including ventricular tachycardia and ventricular fibrillation, are major causes of sudden cardiac death (1). Traditional risk stratification based on left ventricular ejection fraction has limited sensitivity. Artificial intelligence (AI)–based ECG analysis has emerged as a potential tool for automated VA detection (2), but reported performance varies across studies. We performed a systematic review and meta-analysis to evaluate the diagnostic accuracy of AI for VA detection. Methods PubMed, Embase, and Scopus were searched for studies published between January 2014 and February 2026 evaluating AI algorithms for VA detection. Eligible studies reported sensitivity and specificity with test dataset size. Pooled estimates were calculated using a bivariate random-effects model. Summary receiver operating characteristic (SROC) curves were generated, and heterogeneity was assessed using I² statistics. Results Ten studies comprising 586,097 ECG segments (47,633 VA events) were included. Neural networks were used in six studies, with additional classifiers including Support Vector Machine (SVM), Random Forest, K-nearest neighbors (KNN), Deep metric learning, and Classification and Regression Trees (CART). The pooled sensitivity was 96.4% (95% CI 92.2–98.3%), specificity 99.0% (95% CI 97.4–99.5%), and overall accuracy 98.5% (95% CI 96.8–99.3%), with an SROC area under the curve of 0.99. Sensitivity was highest for VT/VF detection (98%). Conclusions AI-based algorithms demonstrate excellent diagnostic performance for ventricular arrhythmia detection across diverse datasets and model architectures. These findings support the potential role of AI-assisted ECG analysis as a scalable tool for arrhythmia surveillance and clinical decision support.
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Sripusanapan et al. (2026) conducted a meta-analysis in Ventricular arrhythmias (including ventricular tachycardia and ventricular fibrillation). Artificial intelligence-based ECG analysis was evaluated on Diagnostic accuracy (sensitivity, specificity, overall accuracy, and SROC AUC) for ventricular arrhythmia detection (Sensitivity 96.4%, Specificity 99.0%, Accuracy 98.5%, SROC AUC 0.99, 95% CI 96.8-99.3). AI-based ECG analysis detected ventricular arrhythmias with 96.4% pooled sensitivity (95% CI 92.2-98.3%), 99.0% specificity (95% CI 97.4-99.5%), and an SROC AUC of 0.99.
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