SynthesisSystematic reviews2025
Artificial intelligence in electrocardiogram signals for sudden cardiac death prediction: a systematic review and meta-analysis.
Synthesis in Systematic reviews, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
1 citing paper in PubMed.
- A Narrative Review of Risk Assessment Approaches for Implantable Defibrillator Therapy to Prevent Sudden Cardiac Death in Acute Myocardial Infarction.Cardiology research and practice · 2026Review
Corrections and comments
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Authors and funding
11 authors.
Funding
Abstract
purposeThe study aimed to evaluate the diagnostic performance of artificial intelligence (AI) in detecting sudden cardiac death on electrocardiogram (ECG).
methodsWe systematically searched PubMed, Web of Science, Embase, and IEEE Xplore for studies published through April 2025 evaluating AI models for ECG-based sudden cardiac death detection, using expert consensus or database records as the reference standard. A bivariate random-effects model generated pooled sensitivity and specificity estimates. Heterogeneity was quantified via I
resultsOut of 958 initially identified studies, 27 studies with 2613 patients and images were ultimately included for the final analysis. For heart rate variability, AI demonstrated a sensitivity of 0.90 (95% CI: 0.86-0.92) and specificity of 0.91 (95% CI: 0.83-0.96), with an AUC of 0.93 (95% CI: 0.91-0.95). For ECG signal segmentation, AI demonstrated a sensitivity of 0.96 (95% CI: 0.92-0.98) and specificity of 0.99 (95% CI: 0.94-1.00), with an AUC of 0.99 (95% CI: 0.98-1.00). For direct input of ECG lead signals, AI demonstrated a sensitivity of 0.87 (95% CI: 0.61-0.97) and specificity of 0.91 (95% CI: 0.75-0.97), with an AUC of 0.95 (95% CI: 0.93-0.97).
conclusionsThis meta-analysis indicates that AI-based ECG analysis shows potential for SCD prediction. However, the summary estimates are derived from highly heterogeneous studies and should not be considered benchmarks for clinical performance. The current evidence remains preliminary and derived from idealized research settings, underscoring the need for prospective, multicenter studies with standardized methodologies to establish generalizability and clinical applicability.
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