Automated assessment of echocardiograms to aid diagnosis of heart failure with preserved ejection fraction

Diagnosis of heart failure with preserved ejection fraction (HFpEF) using the HFA-PEFF and H₂FPEF scores remains challenging in clinical practice and relies on echocardiographic assessment. We aimed to determine whether diagnostic scoring based on automated deep learning interpretation of echocardiograms performs similar to manual measurements in diagnosing HFpEF.

We analysed echocardiograms using an automated deep learning algorithm and manually in three cohorts: a test cohort (102 HFpEF patients diagnosed by right heart catheterization and echocardiography), an ambulatory validation cohort (129 HFpEF patients), and a diagnostic validation cohort (n = 427, of which 182 HFpEF and 245 non-HFpEF patients). We evaluated correlations between automated and manual HFA-PEFF and H₂FPEF scores across cohorts, their correlation with pulmonary capillary wedge pressures (PCWP), and compared diagnostic accuracy using the area under the curve (AUC). Automated and manual measurements showed good agreement across cohorts, with good correlations between HFA-PEFF (0.78–0.86) and H₂FPEF (0.96–0.98) scores and similar correlations with PCWP. One in five patients with high-likelihood HFpEF based on manual HFA-PEFF scores was classified as intermediate-likelihood by automated scores due to lower estimated left atrial volumes, without consistent interaction with atrial fibrillation. Areas under the curve for automated HFA-PEFF and H₂FPEF scores did not consistently differ from manual scores {0.70 [95% confidence interval (CI): 0.66–0.74] vs. 0.71 [95% CI: 0.66–0.75] and 0.78 [95% CI: 0.73–0.82] vs. 0.75 [95% CI: 0.71–0.80], respectively}.

The HFA-PEFF and H₂FPEF scores based on automated and manual echocardiographic analysis showed similar diagnostic accuracy, suggesting automated HFpEF diagnosis using deep learning analysis of echocardiograms is feasible.