Why the study?
Although artificial intelligence (AI)-based LVEF estimation compares reasonably well with conventional readings, its prognostic value in standard transthoracic echocardiography (TTE) and point-of-care ultrasound (POCUS) settings required assessment.
Does artificial intelligence-based estimation of left ventricular ejection fraction accurately predict death and heart failure hospitalization compared to conventional cardiologist readings in patients undergoing standard TTE or POCUS?
Cohort (n=22,475)
Does artificial intelligence-based estimation of left ventricular ejection fraction accurately predict death and heart failure hospitalization compared to conventional cardiologist readings in patients undergoing standard TTE or POCUS?
AI-based LVEF estimation accurately stratifies risk for death and heart failure hospitalization in both standard TTE and non-cardiologist-performed POCUS, outperforming cardiologist review in discriminating mildly reduced from preserved LVEF in the POCUS setting.
Background Artificial intelligence (AI)-based left ventricular ejection fraction (LVEF) estimation compares reasonably well with conventional readings. We assessed its prognostic value in the standard transthoracic echocardiography (TTE) and point-of-care-ultrasound (POCUS) settings. Methods AI-based LVEF estimation was retrospectively tested in 21,920 patients who underwent a standard TTE examination between 2012 and 2021 and prospectively tested in 555 hospitalized patients scanned by non-cardiologists with handheld POCUS devices between 2022 and 2023. Patients were divided into those with preserved (pEF), mildly reduced (mrEF), and significantly reduced LVEF (rEF) using cut-off values of 50% and 40%. Outcome measures were death and heart failure (HF) hospitalization. Results In the TTE cohort, patients in the AImrEF and AIrEF groups had a significantly higher risk for death (HR 1.31 [95% CI 1.20–1.43] and 1.65 [95% CI 1.53–1.77], respectively) and HF hospitalization (HR 2.15 [95% CI 1.74–2.65] and HR 3.16 [95% CI 2.63–3.79], respectively) when compared to AIpEF. Consistent results were obtained in the POCUS group. In the TTE cohort both AI-predicted and cardiologist-reported LVEF clearly discriminated three prognostic trajectories, with superposition of AI-defined and cardiologist-defined curves (p<0.001 for all pairwise comparisons). However, in the POCUS group, only AI-assisted LVEF determination could discriminate the three prognostic trajectories (p<0.001 for all pairwise comparisons), while cardiologist review failed to prognostically separate pEF and mrEF patients (p value=0.95 and 0.9 for death and HF hospitalization, respectively). Conclusions AI-based LVEF estimation accurately predicts outcome in standard TTE as well as in POCUS studies of hospitalized patients performed by non-cardiologists. In the context of POCUS, AI support appears valuable even for the experienced cardiologist reader.
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faierstein et al. (2026) conducted a cohort in Patients undergoing standard transthoracic echocardiography or point-of-care ultrasound for LVEF estimation (n=22,475). AI-based LVEF estimation (reduced and mildly reduced EF) vs. AI-predicted preserved LVEF (AIpEF) and cardiologist-reported LVEF was evaluated on Death and heart failure (HF) hospitalization (HR 1.65, 95% CI 1.53-1.77, p=<0.001).
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