ArticleEuropean heart journal. Imaging methods and practice2025
Real-time guidance and automated measurements using deep learning to improve echocardiographic assessment of left ventricular size and function.
Article in European heart journal. Imaging methods and practice, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT06310330 (Technology Revising Ultrasound Solutions for Tomorrow - Anticancer-treatment Cardiotoxicity Identification by Echocardiography), which is not on this map. Cited by 4 papers.
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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.
Technology Revising Ultrasound Solutions for Tomorrow - Anticancer-treatment Cardiotoxicity Identification by Echocardiography
Who cites it
4 citing papers in PubMed.
- Artificial intelligence in echocardiography: a position statement from the British Society of Echocardiography.Echo research and practice · 2026Review
- Deep Learning for Cardiac Wall Motion Analysis: A Review of Methods, Challenges, and Clinical Applications.Annals of biomedical engineering · 2026Review
- Clinical Applications of Artificial Intelligence in Cardiovascular Imaging: Where Do We Stand?Life (Basel, Switzerland) · 2026Review
- The rise of point-of-care ultrasound in cardiopulmonary diagnostics.European heart journal. Imaging methods and practice · 2026Review
Corrections and comments
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Authors and funding
14 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Aims: The low reproducibility of echocardiographic measurements challenges the identification of subtle changes in left ventricular (LV) function. Deep learning (DL) methods enable real-time analysis of acquisitions and may improve echocardiography. The aim of this study was to evaluate the impact of DL-based guidance and automated measurements on the reproducibility of LV global longitudinal strain (GLS), end-diastolic (EDV) and end-systolic (ESV) volume, and ejection fraction (EF). Methods and results: Forty-six patients (24 breast cancer and 22 general cardiology patients) were included and underwent four consecutive echocardiograms. Six were included twice, totalling 52 inclusions and 208 echocardiograms. One sonographer-cardiologist pair used DL guidance and measurements (DL group), while another did not use DL tools and performed manual measurements (manual group). DL group recordings were also measured using a commercially available DL-based EF tool. For GLS, the DL group had a 30% lower test-retest variability than the manual group (minimal detectable change 2.0 vs. 2.9, Conclusion: Combining real-time DL guidance with automated measurements improved the reproducibility of LV size and function measurements compared with usual care, but future studies are needed to evaluate its clinical effect. Trial registration number: NCT06310330.
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Registered trials
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