Key result
Automated deep learning-derived left atrial reservoir strain accurately identifies HFpEF with an AUC of ~0.81.
Why the study?
HFpEF is challenging to diagnose early, and while left atrial reservoir strain is a sensitive marker, its clinical use is limited by measurement variability and time demands.
Does automated deep learning-derived left atrial reservoir strain analysis provide accurate diagnostic value for early identification of HFpEF?
Observational (n=200)
Does automated deep learning-derived left atrial reservoir strain analysis provide accurate diagnostic value for early identification of HFpEF?
Automated deep learning-based left atrial reservoir strain analysis provides accurate, reproducible, and time-efficient quantification with robust diagnostic value for early HFpEF identification.
Automated DL-derived LARS may aid early HFpEF detection; leaves open prospective validation before clinical adoption.
Background Heart failure with preserved ejection fraction (HFpEF) remains challenging to diagnose due to the absence of overt systolic dysfunction. Left atrial reservoir strain (LARS) has emerged as a sensitive marker of left atrial dysfunction, yet its clinical application is constrained by measurement variability and time demands. We investigated the diagnostic performance of automated deep learning (DL)-derived LARS for early HFpEF identification. Methods This retrospective observational study enrolled 200 patients (107 HFpEF, 93 controls) who underwent transthoracic echocardiography. HFpEF was diagnosed per 2021 ESC guidelines. All echocardiographic datasets were analyzed using a validated DL-based automated platform (EchoGo Pro, Ultromics). Agreement between DL-derived and manually measured parameters was evaluated by Pearson correlation and Bland–Altman analysis. Diagnostic performance was assessed by receiver operating characteristic (ROC) curve analysis, and independent predictors of HFpEF were identified via multivariate logistic regression. Results DL-derived LARS was significantly lower in HFpEF patients compared to controls (19.3 ± 5.7% vs. 30.8 ± 6.4%, P < 0.001). Strong agreement was demonstrated between DL-derived and manually measured LARS (r = 0.911; mean bias − 0.67 ± 2.34%), with superior reproducibility of the automated approach (ICC = 0.974). DL-derived LARS achieved an AUC of 0.806 (95% CI 0.748–0.864), sensitivity of 81.3%, and specificity of 83.9% at a cutoff of 24.2%, comparable to manually derived LARS (AUC = 0.818, P = 0.631) and numerically higher than LAVI and E/e' ratio, although these pairwise differences did not reach statistical significance. On multivariate analysis, DL-derived LARS was the strongest independent echocardiographic predictor of HFpEF (adjusted OR = 0.74, 95% CI 0.65–0.84, P < 0.001). Conclusions Automated DL-based LARS analysis provides accurate, reproducible, and time-efficient quantification comparable to expert manual measurement, demonstrating robust diagnostic value for early HFpEF identification.
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Wang et al. (2026) conducted an observational in Heart failure with preserved ejection fraction (HFpEF) (n=200). Automated deep learning-derived left atrial reservoir strain (LARS) vs. Controls was evaluated on Identification of HFpEF (adjusted OR 0.74, 95% CI 0.65-0.84, p=<0.001). Automated deep learning-derived left atrial reservoir strain accurately identified HFpEF (AUC 0.806) and was a strong independent predictor of the condition (adjusted OR 0.74; 95% CI 0.65-0.84).
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