Key result
AI-ECG detects left ventricular diastolic dysfunction with an AUC of ~0.85 across pooled studies.
Why the study?
Left ventricular diastolic dysfunction is currently diagnosed using echocardiography, which is resource-intensive and operator-dependent, limiting large-scale screening, while AI-enhanced ECG analysis has emerged as a potential scalable alternative with uncertain diagnostic performance.
Does artificial intelligence-enhanced electrocardiography accurately detect left ventricular diastolic dysfunction compared to echocardiography?
Meta-Analysis (n=105,554)
Yes
Does artificial intelligence-enhanced electrocardiography accurately detect left ventricular diastolic dysfunction compared to echocardiography?
Effect estimate: AUC 0.85 (95% CI 0.81-0.87)
AI-enhanced ECG demonstrates good diagnostic performance for detecting LVDD, supporting its potential role in rule-out or risk-enrichment strategies.
AI-ECG may support LVDD rule-out in low-prevalence settings; extends diagnostic data but high heterogeneity leaves prospective validation open.
Left ventricular diastolic dysfunction (LVDD) is an early precursor to heart failure with preserved ejection fraction, 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. This study aims to evaluate the diagnostic accuracy of AI-based algorithms for detecting LVDD in a systematic review and meta-analysis. 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. Five studies including 105 554 participants were analysed. 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%. Artificial intelligence–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.
No takes yet. Share an insight, caveat, or question.
Johann et al. (2026) conducted a meta-analysis in Left ventricular diastolic dysfunction (LVDD) (n=105,554). Artificial intelligence-enhanced electrocardiography vs. Echocardiography (reference standard) was evaluated on Area under the summary receiver operating characteristic curve (AUC) for detecting LVDD (AUC 0.85, 95% CI 0.81-0.87). Artificial intelligence-enhanced electrocardiography demonstrated a pooled sensitivity of 0.82, specificity of 0.77, and an AUC of 0.85 for detecting left ventricular diastolic dysfunction.
Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context: