Key result
AI-enabled echocardiography detects HFpEF and elevated filling pressures with ~25-fold higher diagnostic odds.
Why the study?
HFpEF remains difficult to diagnose non-invasively, and although AI applied to echocardiography may improve detection, its aggregate diagnostic accuracy had not been quantified.
Does AI-enabled echocardiography accurately detect heart failure with preserved ejection fraction, elevated left ventricular filling pressure, or diastolic dysfunction compared to invasive or guideline reference standards?
Meta-Analysis (n=2,770)
Does AI-enabled echocardiography accurately detect heart failure with preserved ejection fraction, elevated left ventricular filling pressure, or diastolic dysfunction compared to invasive or guideline reference standards?
AI-enabled echocardiography demonstrates good pooled diagnostic accuracy (sensitivity 84%, specificity 83%, AUC 0.875) for non-invasively detecting HFpEF and elevated filling pressures, though high heterogeneity and reference-standard dependence necessitate further prospective validation.
AI echocardiography may aid noninvasive HFpEF detection in practice; leaves open need for prospective outcome trials before adoption.
Heart failure with preserved ejection fraction (HFpEF) accounts for nearly half of heart failure cases and remains difficult to diagnose non-invasively. Artificial intelligence (AI) applied to echocardiography may improve detection, but its aggregate diagnostic accuracy has not been quantified. We performed a diagnostic test accuracy meta-analysis of AI-enabled echocardiography for identifying HFpEF, elevated left ventricular filling pressure, or diastolic dysfunction. MEDLINE/PubMed, Embase, and CENTRAL were searched from inception to June 2026. Studies reporting the accuracy of AI models applied to echocardiography against invasive, guideline-adjudicated, or ASE/EACVI reference standards were included. Sensitivity and specificity were pooled using a bivariate random-effects (Reitsma) model, with a summary receiver operating characteristic (ROC) curve, diagnostic odds ratio, and likelihood ratios. Risk of bias was assessed with QUADAS-2. Eight studies (2770 participants; 1317 [47.5%] with the target condition) were included. Pooled sensitivity was 84.0% (95% CI: 73.0-91.0%) and specificity 83.0% (95% CI: 73.0-89.0%), with an area under the summary ROC curve of 0.875 (95% CI: 0.791-0.930). The diagnostic odds ratio was 24.73 (95% CI: 18.46-33.12); positive and negative likelihood ratios were 4.84 and 0.20. Heterogeneity was high. Performance did not differ by AI architecture but varied significantly by reference standard. No small-study effects were detected. AI-enabled echocardiography shows good pooled accuracy for detecting HFpEF and elevated filling pressures, supporting its diagnostic potential. High heterogeneity and reference-standard dependence warrant larger, prospectively validated studies before routine clinical adoption.
No takes yet. Share an insight, caveat, or question.
Al-Harbi et al. (2026) conducted a meta-analysis in Heart failure with preserved ejection fraction (HFpEF), elevated left ventricular filling pressure, or diastolic dysfunction (n=2,770). AI-enabled echocardiography vs. Invasive, guideline-adjudicated, or ASE/EACVI reference standards was evaluated on Diagnostic accuracy for identifying HFpEF, elevated left ventricular filling pressure, or diastolic dysfunction (OR 24.73, 95% CI 18.46-33.12). AI-enabled echocardiography detected HFpEF and elevated filling pressures with 84.0% sensitivity, 83.0% specificity, and a diagnostic OR of 24.73 (95% CI 18.46-33.12).
Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context: