ArticleFrontiers in cardiovascular medicine2021
Deep Learning Analysis of Echocardiographic Images to Predict Positive Genotype in Patients With Hypertrophic Cardiomyopathy.
Article in Frontiers in cardiovascular medicine, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers.
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Who cites it
17 citing papers in PubMed, 25 citations in OpenAlex.
- Artificial intelligence in echocardiography: Applications and future directions.Fundamental research · 2026Review
- Advancements and Applications of Artificial Intelligence in Hypertrophic Cardiomyopathy: A Comprehensive Review.Reviews in cardiovascular medicine · 2026Review
- Multi-view deep learning for echocardiogram interpretation.Nature cardiovascular research · 2026Article
- Inter-machine harmonization of multicenter echocardiographic images for improvement of left ventricular ejection fraction prediction model.Scientific reports · 2025Article
- Artificial intelligence applications in hypertrophic cardiomyopathy: turns and loopholes.European heart journal. Digital health · 2025Review
- The Evolving Role of Cardiac Imaging in Hypertrophic Cardiomyopathy: Diagnosis, Prognosis, and Clinical Practice.Biomedicines · 2025Review
- Machine Learning Computer Vision Point of Care Decision Support of Echocardiographic Identification of Hypertrophic Cardiomyopathy.JACC. Advances · 2025Article
- Hearts, Data, and Artificial Intelligence Wizardry: From Imitation to Innovation in Cardiovascular Care.Biomedicines · 2025Review
- Review
- Transforming Echocardiography: The Role of Artificial Intelligence in Enhancing Diagnostic Accuracy and Accessibility.Internal medicine (Tokyo, Japan) · 2025Review
- Deep learning of echocardiography distinguishes between presence and absence of late gadolinium enhancement on cardiac magnetic resonance in patients with hypertrophic cardiomyopathy.Echo research and practice · 2024Article
- Review
- Prediction of malaria positivity using patients' demographic and environmental features and clinical symptoms to complement parasitological confirmation before treatment.Tropical diseases, travel medicine and vaccines · 2023Article
- Revolution of echocardiographic reporting: the new era of artificial intelligence and natural language processing.Journal of echocardiography · 2023Review
- Artificial intelligence-assisted interpretation of systolic function by echocardiogram.Open heart · 2023Article
- Machine Learning Approaches in Diagnosis, Prognosis and Treatment Selection of Cardiac Amyloidosis.International journal of molecular sciences · 2023Review
- Multinational Federated Learning Approach to Train ECG and Echocardiogram Models for Hypertrophic Cardiomyopathy Detection.Circulation · 2022Article
Corrections and comments
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Authors and funding
10 authors at 3 institutions in 2 countries.
Funding
Abstract
Genetic testing provides valuable insights into family screening strategies, diagnosis, and prognosis in patients with hypertrophic cardiomyopathy (HCM). On the other hand, genetic testing carries socio-economical and psychological burdens. It is therefore important to identify patients with HCM who are more likely to have positive genotype. However, conventional prediction models based on clinical and echocardiographic parameters offer only modest accuracy and are subject to intra- and inter-observer variability. We therefore hypothesized that deep convolutional neural network (DCNN, a type of deep learning) analysis of echocardiographic images improves the predictive accuracy of positive genotype in patients with HCM. In each case, we obtained parasternal short- and long-axis as well as apical 2-, 3-, 4-, and 5-chamber views. We employed DCNN algorithm to predict positive genotype based on the input echocardiographic images. We performed 5-fold cross-validations. We used 2 reference models-the Mayo HCM Genotype Predictor score (Mayo score) and the Toronto HCM Genotype score (Toronto score). We compared the area under the receiver-operating-characteristic curve (AUC) between a combined model using the reference model plus DCNN-derived probability and the reference model. We calculated the
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.