ArticleEuropean heart journal. Imaging methods and practice2026
Assessment of HeartModel® automated left ventricular ejection fraction for patients with hypertrophic cardiomyopathy.
Article in European heart journal. Imaging methods and practice, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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Who cites it
3 citing papers in PubMed.
- Artificial intelligence-powered three-dimensional echocardiography in clinical practice: the illusion of automation?European heart journal. Imaging methods and practice · 2026Article
- Does the reported learning curve of artificial intelligence-guided point-of-care ultrasound reflect operator competency or study design?European heart journal. Imaging methods and practice · 2026Article
- Artificial intelligence-empowered echocardiography: an updated review of clinical management in hypertrophic cardiomyopathy.Frontiers in cardiovascular medicine · 2026Review
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Authors and funding
10 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Aims: Cardiac myosin inhibitors (CMIs) have revolutionized care for patients with obstructive hypertrophic cardiomyopathy (HCM), however they are associated with a risk of systolic dysfunction. Machine learning algorithms might expand access to frequent accurate assessment of left ventricular ejection fraction (LVEF). We assess the performance of a commercial ML-based LVEF model for patients with HCM. Methods and results: Single centre prospective study of measurements of left ventricular function by Philips HeartModel® (automated) assessment, echocardiographer assessment (standard), and cardiac magnetic resonance imaging for patients with HCM. Assessments of LVEF, end diastolic volume and end systolic volume were studied across methods. 50 patients with HCM were included. Median age 64 years; 64% male; and 62% had cMRI data for analysis. Median automated LVEF was lower than standard [55.5% (IQR 9) vs. 62.5% (IQR 10), Conclusion: For patients with HCM, automated assessments of LV size and function differ significantly from standard assessments, raising concerns about the use of this ML-enabled LVEF software for this patient population and potential application to guiding CMI treatments.
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