Artificial Intelligence-Enabled Electrocardiography for Detection of Left Ventricular Diastolic Dysfunction: A Systematic Review and Meta-Analysis

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ID: 325268
2026
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Abstract
Abstract Background Left ventricular diastolic dysfunction (LVDD) is an early precursor to heart failure with preserved ejection fraction (HFpEF) and it is currently diagnosed using echocardiography, a resource-intensive and operator-dependent modality that limits large scale screening. The 12-lead electrocardiogram (ECG) is widely available but lacks sufficient diagnostic accuracy for LVDD. Artificial intelligence (AI)-enhanced ECG analysis has emerged as a potential scalable alternative, although its overall diagnostic performance remains uncertain. Objectives To evaluate the diagnostic accuracy of AI-based algorithms for detecting LVDD in a systematic review and meta-analysis. Methods We systematically searched eight major databases through August 2025, complemented by forward and backward citation chasing. Studies reporting sensitivity and specificity of AI-ECG models, using echocardiography as the reference standard, were included. Pooled sensitivity, specificity, and area under the summary receiver operating characteristic curve (AUC) were estimated using a bivariate random-effects model. Results Five studies including 105,554 participants were analyzed. AI-ECG demonstrated a pooled sensitivity of 0.82 (95% CI: 0.81–0.83) and specificity of 0.77 (95% CI: 0.70–0.82), with an AUC of 0.85 (95% CI: 0.81–0.87). Substantial heterogeneity was observed (I2 = 98.5% for sensitivity and 99.8% for specificity), although results were robust in sensitivity analyses. Predictive values were prevalence-dependent. Negative predictive value was 98.8% at 5% prevalence and 93.7% at 22.2%, but declined to 81.1% at 50% prevalence and 64.7% at 70%. Conclusion AI-enhanced electrocardiography demonstrated good diagnostic performance for detecting LVDD and may support future rule-out or risk-enrichment strategies in selected populations. However, current evidence remains insufficient to support routine clinical implementation.
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Authors Edjimbi Johann, Nisarg Shah, L. Donisi, Aryan Gajjar, Claudia See, Alyssa Grimshaw, Hassim Bachir Diop, Antonio Luiz P Ribeiro, Éloi Marijon, Gari Clifford, Bamba Gaye
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Journal European Heart Journal - Digital Health
Year 2026
DOI
10.1093/ehjdh/ztag130
URL
https://doi.org/10.1093/ehjdh/ztag130
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