ArticleEuropean heart journal. Digital health2025
Echocardiographic measures read by artificial intelligence enable accurate and rapid prediction of the worsening of heart failure.
Article in European heart journal. Digital health, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT04064450 (MyoVasc - An Epidemiological Cohort Study to Investigate the Development and Progression of Heart Failure and the Interaction With Vascular Disease), which is not on this map. Cited by 2 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
MyoVasc - An Epidemiological Cohort Study to Investigate the Development and Progression of Heart Failure and the Interaction With Vascular Disease
Who cites it
2 citing papers in PubMed.
- Echocardiography-based multimodal models for thromboembolic risk prediction in atrial fibrillation: a narrative review.Journal of thoracic disease · 2026Review
- Diagnostic Tests for Stage B Heart Failure.Current cardiology reports · 2026Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
8 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Aims: Automatic echocardiographic measurements using artificial intelligence have shown promising results; however, they have not been compared with manual measurements regarding heart failure (HF) progression and algorithm runtime. Methods and results: Data came from the prospective HF study MyoVasc (NCT04064450), which involved a highly standardized 5-h examination, including comprehensive echocardiography, at a dedicated study centre between January 2013 and April 2018. Worsening of HF was a primary composite endpoint, recorded by structured follow-up, death certificates, and medical records. The automated assessment was performed using EchoDL, eight 3D convolutional neural networks (CNNs) trained to predict clinical parameters. Manual and automatic left ventricular ejection fraction (LVEF), Conclusion: Automated analysis of echocardiograms using 3D CNNs was comparable to manual measurements in predicting HF-specific outcomes. Echo-DL offers potential time savings and improved risk prediction in clinical settings, allowing integration into echocardiographic hardware.
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Registered trials
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