ArticleBMC medical imaging2024
A deep learning based method for left ventricular strain measurements: repeatability and accuracy compared to experienced echocardiographers.
Article in BMC medical imaging, 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-assisted left ventricular global longitudinal strain assessment in patients with acute myocardial infarction: a RESUS-AMI trial sub-analysis.The international journal of cardiovascular imaging · 2025Trial
- Deep Learning for Cardiac Wall Motion Analysis: A Review of Methods, Challenges, and Clinical Applications.Annals of biomedical engineering · 2026Review
- Block Matching Based Speckle Tracking Echocardiography: Clinical Applications and Research Outlook in a Deep Learning Context.Journal of imaging informatics in medicine · 2026Article
- Artificial Intelligence in Cardiovascular Ultrasound: Clinical Applications, Foundation Models, and the Path to Precision Cardiology.Journal of clinical medicine · 2026Review
- Artificial Intelligence Across the Echocardiographic Workflow: A Narrative Review for Clinicians.Cureus · 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
- Deep-learning-based estimation of left ventricle myocardial strain from echocardiograms with occlusion artifacts.Journal of medical imaging (Bellingham, Wash.) · 2025Article
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Authors and funding
14 authors.
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Abstract
backgroundSpeckle tracking echocardiography (STE) provides quantification of left ventricular (LV) deformation and is useful in the assessment of LV function. STE is increasingly being used clinically, and every effort to simplify and standardize STE is important. Manual outlining of regions of interest (ROIs) is labor intensive and may influence assessment of strain values. PURPOSE: We hypothesized that a deep learning (DL) model, trained on clinical echocardiographic exams, can be combined with a readily available echocardiographic analysis software, to automate strain calculation with comparable fidelity to trained cardiologists.
methodsData consisted of still frame echocardiographic images with cardiologist-defined ROIs from 672 clinical echocardiographic exams from a university hospital outpatient clinic. Exams included patients with ischemic heart disease, heart failure, valvular disease, and conduction abnormalities, and some healthy subjects. An EfficientNetB1-based architecture was employed, and different techniques and properties including data set size, data quality, augmentations, and transfer learning were evaluated. DL predicted ROIs were reintroduced into commercially available echocardiographic analysis software to automatically calculate strain values.
resultsDL-automated strain calculations had an average absolute difference of 0.75 (95% CI 0.58-0.92) for global longitudinal strain (GLS), and 1.16 (95% CI 1.03-1.29) for single-projection longitudinal strain (LS), compared to operators. A Bland-Altman plot revealed no obvious bias, though there were fewer outliers in the lower average LS ranges. Techniques and data properties yielded no significant increase/decrease in performance.
conclusionThe study demonstrates that DL-assisted, automated strain measurements are feasible, and provide results within interobserver variation. Employing DL in echocardiographic analyses could further facilitate adoption of STE parameters in clinical practice and research, and improve reproducibility.
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