ArticleAnnals of translational medicine2021
Artificial intelligence-based myocardial texture analysis in etiological differentiation of left ventricular hypertrophy.
Article in Annals of translational medicine, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers.
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Who cites it
18 citing papers in PubMed, 28 citations in OpenAlex.
- Point-of-care echocardiography screening for hypertrophic cardiomyopathy using automated deep-learning analysis.European heart journal. Digital health · 2026Article
- Quantitative imaging analysis in hypertrophic cardiomyopathy: phenotypic differentiation to prognostic stratification.European heart journal. Imaging methods and practice · 2026Review
- Artificial intelligence in echocardiography: a position statement from the British Society of Echocardiography.Echo research and practice · 2026Review
- Applications of artificial intelligence and computational approaches to imaging for hypertension identification, phenotyping, and outcome prediction: a systematic review.European heart journal. Digital health · 2026Review
- Clinical Applications of Artificial Intelligence in Cardiovascular Imaging: Where Do We Stand?Life (Basel, Switzerland) · 2026Review
- Advances in Non-Invasive Myocardial Stiffness Assessment and Clinical Applications in Hypertrophic Cardiomyopathy.Reviews in cardiovascular medicine · 2026Review
- Artificial intelligence-empowered echocardiography: an updated review of clinical management in hypertrophic cardiomyopathy.Frontiers in cardiovascular medicine · 2026Review
- Artificial intelligence applications in hypertrophic cardiomyopathy: turns and loopholes.European heart journal. Digital health · 2025Review
- Left Ventricular Myocardial Dysfunction Evaluation in Thalassemia Patients Using Echocardiographic Radiomic Features and Machine Learning Algorithms.Journal of digital imaging · 2023Article
- Advance of echocardiography in cardiac amyloidosis.Heart failure reviews · 2023Review
- Deep learn-based computer-assisted transthoracic echocardiography: approach to the diagnosis of cardiac amyloidosis.The international journal of cardiovascular imaging · 2023Article
- The Role of Artificial Intelligence in Echocardiography.Journal of imaging · 2023Review
- Incremental predictive utility of a radiomics signature in a nomogram for the recurrence of atrial fibrillation.Frontiers in cardiovascular medicine · 2023Article
- Novel Hypertrophic Cardiomyopathy Diagnosis Index Using Deep Features and Local Directional Pattern Techniques.Journal of imaging · 2022Article
- Artificial intelligence and imaging: Opportunities in cardio-oncology.American heart journal plus : cardiology research and practice · 2022Article
- Artificial Intelligence and Echocardiography.Journal of cardiovascular imaging · 2021Review
- Review
- The Expression of miR-365 in Serum of Hypertension Patients with Left Ventricular Hypertrophy Was Up-Regulated, Which Was Positively Correlated with Left Ventricular Mass Index.Pharmacogenomics and personalized medicine · 2021Article
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Authors and funding
6 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundTransthoracic echocardiography (TTE) is widely used in clinics to evaluate left ventricular hypertrophy (LVH). However, TTE is usually insufficient for the etiological diagnoses when morphological and functional features are nonspecific. With the booming of computer science and artificial intelligence (AI), previous literature has reported the application of radiomics based on cardiac magnetic resonance imaging, cardiac computed tomography and TTE in diagnosing several myocardial abnormalities, such as myocardial infarction, myocarditis, cardiac amyloidosis, and hypertrophic cardiomyopathy (HCM). In this study, we explored the possibility of using myocardial texture features in differentiating HCM, hypertensive heart disease (HHD) and uremic cardiomyopathy (UCM) based on echocardiography. To our knowledge, this was the first study to explore TTE myocardial texture analysis for multiple LVH etiology differentiation.
methodsTTE images were reviewed retrospectively from January 2018 to collect 50 cases for each group of HHD, HCM and UCM. The apical four chamber view was retrieved. Seventeen first-order statistics and 60 gray level co-occurrence matrix (GLCM) features were extracted for statistics and classification test by support vector machine (SVM).
resultsOf all the parameters, entropy of brightness (EtBrt), standard deviation (Std), coefficient of variation (CoV), skewness (Skew), contrast7 (Cont7) and homogeneity5 (Hm5) were found statistically significant among the three groups (all P<0.05) and with acceptable reproducibility (intraobserver and interobserver ICC >0.50). As a result, HCM showed the most homogeneous myocardial texture, and was significantly different from HHD and UCM (all six features: P≤0.005). HHD appeared slightly more homogeneous than UCM, as only EtBrt and CoV were significant (P=0.011 and P=0.008). According to higher areas under the receiver operating characteristic curve (AUC) (>0.50), EtBrt, Std, and CoV were selected for test of classification as a combination of features. The AUC derived from SVM model was slightly improved compared with those of EtBrt, Std and CoV individually.
conclusionsAI-based myocardial texture analysis using ultrasonic images may be a potential approach to aiding LVH etiology differentiation.
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