ArticleThe international journal of cardiovascular imaging2022
Using deep learning method to identify left ventricular hypertrophy on echocardiography.
Article in The international journal of cardiovascular imaging, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 22 papers, 1 of them a synthesis that pooled it.
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Who cites it
22 citing papers in PubMed, 1 synthesis or guideline pooled it, 38 citations in OpenAlex.
- Machine Learning in Left Ventricular Hypertrophy Detection: Systematic Review and Meta-Analysis.Journal of medical Internet research · 2026Pooled it
- Point-of-care echocardiography screening for hypertrophic cardiomyopathy using automated deep-learning analysis.European heart journal. Digital health · 2026Article
- Curriculum-Based Deep Learning for Automated Segmental Left Ventricular Hypertrophy Classification Using Short-Axis Echocardiography.Diagnostics (Basel, Switzerland) · 2026Article
- Quantitative imaging analysis in hypertrophic cardiomyopathy: phenotypic differentiation to prognostic stratification.European heart journal. Imaging methods and practice · 2026Review
- Smarter FoCUS: AI-guided focused cardiac ultrasound enables novice detection of left ventricular dysfunction.European heart journal. Digital health · 2026Article
- Real-time deep learning interpretation of echocardiographic video for automated detection of anatomical features associated with tetralogy of fallot in pediatric patients : a feasibility study.Scientific reports · 2026Article
- Clinical Applications of Artificial Intelligence in Cardiovascular Imaging: Where Do We Stand?Life (Basel, Switzerland) · 2026Review
- Artificial intelligence-enhanced echocardiography in cardiovascular disease management.Nature reviews. Cardiology · 2026Review
- Artificial intelligence-empowered echocardiography: an updated review of clinical management in hypertrophic cardiomyopathy.Frontiers in cardiovascular medicine · 2026Review
- Multimodal echocardiographic techniques in the diagnosis of cardiac tumors: applications and recent advances.Frontiers in cardiovascular medicine · 2026Review
- Hypertension and left ventricular geometry: Diagnosis, prognosis, and management.EXCLI journal · 2026Review
- A unified multi-task learning framework for automated assessment of left ventricular structure and its systolic function from echocardiography.Scientific reports · 2025Article
- Echocardiography Combined With Radiomics and Deep Transfer Learning to Diagnose Hypertrophic Cardiomyopathy and Other Etiologies of Left Ventricular Hypertrophy: A Multicenter Study Comparing the Performance With Echocardiologists.Reviews in cardiovascular medicine · 2025Article
- Value of Artificial Intelligence for Enhancing Suspicion of Cardiac Amyloidosis Using Electrocardiography and Echocardiography: A Narrative Review.Journal of the American Heart Association · 2025Review
- 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
- Advance of echocardiography in cardiac amyloidosis.Heart failure reviews · 2023Review
- Challenges and solutions of echocardiography generalization for deep learning: a study in patients with constrictive pericarditis.Journal of medical imaging (Bellingham, Wash.) · 2023Article
- Multi-channel deep learning model-based myocardial spatial-temporal morphology feature on cardiac MRI cine images diagnoses the cause of LVH.Insights into imaging · 2023Article
- Developing an Echocardiography-Based, Automatic Deep Learning Framework for the Differentiation of Increased Left Ventricular Wall Thickness Etiologies.Journal of imaging · 2023Article
Corrections and comments
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Authors and funding
8 authors at 3 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundLeft ventricular hypertrophy (LVH) is an independent prognostic factor for cardiovascular events and it can be detected by echocardiography in the early stage. In this study, we aim to develop a semi-automatic diagnostic network based on deep learning algorithms to detect LVH.
methodsWe retrospectively collected 1610 transthoracic echocardiograms, included 724 patients [189 hypertensive heart disease (HHD), 218 hypertrophic cardiomyopathy (HCM), and 58 cardiac amyloidosis (CA), along with 259 controls]. The diagnosis of LVH was defined by two experienced clinicians. For the deep learning architecture, we introduced ResNet and U-net++ to complete classification and segmentation tasks respectively. The models were trained and validated independently. Then, we connected the best-performing models to form the final framework and tested its capabilities.
resultsIn terms of individual networks, the view classification model produced AUC = 1.0. The AUC of the LVH detection model was 0.98 (95% CI 0.94-0.99), with corresponding sensitivity and specificity of 94.0% (95% CI 85.3-98.7%) and 91.6% (95% CI 84.6-96.1%) respectively. For etiology identification, the independent model yielded good results with AUC = 0.90 (95% CI 0.82-0.95) for HCM, AUC = 0.94 (95% CI 0.88-0.98) for CA, and AUC = 0.88 (95% CI 0.80-0.93) for HHD. Finally, our final integrated framework automatically classified four conditions (Normal, HCM, CA, and HHD), which achieved an average of AUC 0.91, with an average sensitivity and specificity of 83.7% and 90.0%.
conclusionDeep learning architecture has the ability to detect LVH and even distinguish the latent etiology of LVH.
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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.