Key result
Novel echocardiographic AI model for detecting HFpEF requires validation against invasive hemodynamics.
Why the study?
While AI models applied to echocardiograms show promise for HFpEF discrimination, their correlation with gold-standard invasive hemodynamics has yet to be evaluated.
Does a novel echocardiographic AI model correlate with invasive hemodynamics for the detection of HFpEF?
Observational
Does a novel echocardiographic AI model correlate with invasive hemodynamics for the detection of HFpEF?
This text outlines the rationale for evaluating the correlation between an echocardiographic AI model and invasive hemodynamics for detecting HFpEF, but provides no results.
Answers are grounded in this paper and cite their sources.
Cannot yet guide HFpEF diagnosis; leaves open correlation with invasive hemodynamics pending quantitative validation.
Heart failure (HF) affects up to 1 in 4 adults in their lifetime; half of these patients are classified as HF with preserved ejection fraction (HFpEF).1 HFpEF is a highly heterogenous syndrome, making diagnosis complex without invasive hemodynamic assessment.2 Artificial intelligence (AI) applied to transthoracic echocardiograms (TTE) has demonstrated excellent discrimination of HFpEF, more often than clinical scores.3 While initial validation of such models have shown promise, their correlation with gold-standard invasive hemodynamics has yet to be evaluated.
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Hamid et al. (2026) conducted an observational in Heart Failure with Preserved Ejection Fraction (HFpEF). Echocardiographic AI Model vs. Invasive hemodynamics was evaluated on Correlation with invasive hemodynamics. A novel echocardiographic AI model for detecting HFpEF requires evaluation against gold-standard invasive hemodynamics, as no quantitative results are reported in the abstract.
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