ArticleEuropean heart journal. Imaging methods and practice2024
Deep learning improves test-retest reproducibility of regional strain in echocardiography.
Article in European heart journal. Imaging methods and practice, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed.
- Artificial Intelligence in Cardiovascular Ultrasound: Clinical Applications, Foundation Models, and the Path to Precision Cardiology.Journal of clinical medicine · 2026Review
- Deep Learning for Cardiac Wall Motion Analysis: A Review of Methods, Challenges, and Clinical Applications.Annals of biomedical engineering · 2026Review
- Automatic monitoring of single-wall MAPSE by transesophageal echocardiography for tracking global left ventricular function irrespective of regional hypokinesia: a secondary analysis.Intensive care medicine experimental · 2026Article
- 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
- Artificial intelligence-enhanced echocardiography in cardiovascular disease management.Nature reviews. Cardiology · 2026Review
- Optimizing imaging techniques in hypertrophic cardiomyopathy: challenges and opportunities.European heart journal. Imaging methods and practice · 2026Review
- Generative augmentations for improved cardiac ultrasound segmentation using diffusion models.Scientific reports · 2025Article
Corrections and comments
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Authors and funding
14 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Aims: The clinical utility of regional strain measurements in echocardiography is challenged by suboptimal reproducibility. In this study, we aimed to evaluate the test-retest reproducibility of regional longitudinal strain (RLS) per coronary artery perfusion territory (RLS Methods and results: We measured strain in a dual-centre test-retest data set that included 40 controls and 40 patients with suspected non-ST elevation acute coronary syndrome. Two consecutive echocardiograms per subject were recorded by different operators. The reproducibility of RLS Conclusion: The novel DL method provided fully automated measurements of RLS, with improved test-retest reproducibility compared with semi-automatic measurements by experienced observers. RLS measured by the DL method has the potential to advance patient care through a more detailed, more efficient, and less user-dependent clinical assessment of myocardial function.
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Registered trials
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