ArticleEuropean heart journal. Digital health2024
Population data-based federated machine learning improves automated echocardiographic quantification of cardiac structure and function: the
Article in European heart journal. Digital health, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 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.
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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.
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Who cites it
6 citing papers in PubMed.
- Artificial intelligence in echocardiography: Applications and future directions.Fundamental research · 2026Review
- Reproducibility of echocardiographic measurements of left ventricular systolic function: a systematic review and meta-analysis comparing artificial intelligence and clinician estimates.European heart journal. Digital health · 2026Review
- Challenges and opportunities in assessing right ventricular structure and function: a Roadmap for standardization, clinical implementation and research.Nature reviews. Cardiology · 2026Review
- Federated learning for cardiovascular disease prediction: a systematic review of clinical applications, validation, and translation readiness.Frontiers in cardiovascular medicine · 2026Review
- Development and Validation of Echocardiography Artificial Intelligence Models: A Narrative Review.Journal of clinical medicine · 2025Review
- 3D echocardiography derived reference values and determinants of left ventricular twist and torsion from the population-based STAAB cohort study.Scientific reports · 2025Article
Corrections and comments
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Authors and funding
16 authors.
Funding
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Abstract
Aims: Machine-learning (ML)-based automated measurement of echocardiography images emerges as an option to reduce observer variability. The objective of the study is to improve the accuracy of a pre-existing automated reading tool ('original detector') by federated ML-based re-training. Methods and results: Conclusion: Population data-based ML in a federated ML set-up was feasible. The re-trained detector exhibited a much lower measurement variability than human readers. This gain in accuracy and precision strengthens the confidence in automated echocardiographic readings, which carries large potential for applications in various settings.
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Registered trials
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