ArticleJACC. Advances2025
Machine Learning Computer Vision Point of Care Decision Support of Echocardiographic Identification of Hypertrophic Cardiomyopathy.
Article in JACC. Advances, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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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
5 citing papers in PubMed.
- Point-of-care echocardiography screening for hypertrophic cardiomyopathy using automated deep-learning analysis.European heart journal. Digital health · 2026Article
- Synthetic Echocardiograms from Diffusion Models in Rare Cardiovascular Disease.medRxiv : the preprint server for health sciences · 2026Article
- Detection of Left Ventricular Outflow Obstruction From Standard B-Mode Echocardiogram Videos Using Deep Learning.JACC. Advances · 2026Article
- AI-Enabled Precision Echocardiography: Toward Personalized Cardiovascular Care.Diagnostics (Basel, Switzerland) · 2026Review
- Artificial intelligence-empowered echocardiography: an updated review of clinical management in hypertrophic cardiomyopathy.Frontiers in cardiovascular medicine · 2026Review
Corrections and comments
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Authors and funding
10 authors.
Funding
Abstract
backgroundHypertrophic cardiomyopathy (HCM) remains underdiagnosed, and artificial intelligence tools for echocardiographic recognition have been hampered by lack of insight into drivers of model predictions and ease of implementation.
objectivesThe purpose of this study was to train and validate a machine learning model with visualization of model prediction, optimized for implementation, to identify HCM from echocardiography.
methods1,601 HCM cases from 2000 to 2021 were matched on age, sex, year of echo, and ejection fraction to 7,103 controls from Duke Medical Center. Multivendor echocardiograms were used to train and validate a convolutional neural network (CNN) identifying HCM. Saliency maps were produced for insight into model predictions. CNN performance was evaluated by receiving operating characteristic and precision recall and additionally investigated among a subset of 232 patients with cardiac magnetic resonance imaging-based HCM morphologic grading.
resultsAmong the 1,601 HCM cases, the median age was 61, with 46.9% male. The median ejection fraction was 55% with 12.4% having an ejection fraction <50%. Median septal wall thickness was 1.6 (IQR) and 28.6% had obstructive HCM. Matched controls had similar demographics and ejection fraction. Model area under the curve when using the 4-chamber view was 0.84 with precision of 0.68. Among patients with cardiac magnetic resonance imaging-based HCM morphology, area under the curve was >0.95 for all morphologies with precision between 0.16 and 0.72. Saliency maps demonstrated maximum intensity within ventricular myocardium.
conclusionsA machine learning CNN with model prediction visualization optimized for clinical implementation identifies all HCM morphologic subtypes. Further work is necessary to validate model performance in external data and real-world use.
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