ArticleScientific reports2021
Machine learning of native T1 mapping radiomics for classification of hypertrophic cardiomyopathy phenotypes.
Article in Scientific reports, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 27 papers, 1 of them a synthesis that pooled it.
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Who cites it
27 citing papers in PubMed, 1 synthesis or guideline pooled it, 51 citations in OpenAlex.
- Machine learning-based methods in diagnosing cardiac amyloidosis: a meta-analysis.Frontiers in cardiovascular medicine · 2026Pooled it
- Cardiac Magnetic Resonance Radiomics for Diagnosis, Phenotyping and Risk Stratification of Cardiomyopathies.Healthcare (Basel, Switzerland) · 2026Review
- A Multitask Deep Learning Pipeline for Classifying Hypertrophic Cardiomyopathy and Hypertensive Heart Disease Based on Native T1 Mapping.Journal of imaging informatics in medicine · 2026Article
- Quantitative imaging analysis in hypertrophic cardiomyopathy: phenotypic differentiation to prognostic stratification.European heart journal. Imaging methods and practice · 2026Review
- Detection of Left Ventricular Outflow Obstruction From Standard B-Mode Echocardiogram Videos Using Deep Learning.JACC. Advances · 2026Article
- Decoding the Heart Through Computed Tomography: Early Cardiomyopathy Detection Using Ensemble-Based Segmentation and Radiomics.Journal of imaging · 2026Article
- Myocardial radiomics of non-ischemic cardiomyopathy using cardiovascular magnetic resonance: current perspectives and future directions.Frontiers in cardiovascular medicine · 2026Review
- T1 Mapping–derived Predictors of Cardiac Remodeling and Fibrosis in Athletes using Advanced Machine Learning TechniquesCurrent medical imaging · 2026Article
- Assessing the quality of reporting in artificial intelligence/machine learning research for cardiac amyloidosis.JAMIA open · 2025Article
- Artificial intelligence applications in hypertrophic cardiomyopathy: turns and loopholes.European heart journal. Digital health · 2025Review
- Artificial Intelligence in Nuclear Cardiac Imaging: Novel Advances, Emerging Techniques, and Recent Clinical Trials.Journal of clinical medicine · 2025Review
- Texture analysis of cardiovascular MRI native T1 mapping in patients with Duchenne muscular dystrophy.Orphanet journal of rare diseases · 2025Article
- Repeatability of radiomic features in myocardial T1 and T2 mapping.European radiology · 2025Article
- Leveraging a Vision Transformer Model to Improve Diagnostic Accuracy of Cardiac Amyloidosis With Cardiac Magnetic Resonance.JACC. Cardiovascular imaging · 2025Article
- Cardiac amyloidosis: Innovations in diagnosis and treatment.European heart journal supplements : journal of the European Society of Cardiology · 2025Article
- Next-Generation Cardiac Magnetic Resonance Imaging Techniques for Characterization of Myocardial Disease.Current treatment options in cardiovascular medicine · 2024Article
- Mitigating the impact of image processing variations on tumour [Scientific reports · 2024Article
- A structural heart-brain axis mediates the association between cardiovascular risk and cognitive function.Imaging neuroscience (Cambridge, Mass.) · 2024Article
- Artificial Intelligence in Cardiology and Atherosclerosis in the Context of Precision Medicine: A Scoping Review.Applied bionics and biomechanics · 2024Article
- Prediction of incident cardiovascular events using machine learning and CMR radiomics.European radiology · 2023Article
Corrections and comments
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Authors and funding
12 authors at 2 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
We explored whether radiomic features from T1 maps by cardiac magnetic resonance (CMR) could enhance the diagnostic value of T1 mapping in distinguishing health from disease and classifying cardiac disease phenotypes. A total of 149 patients (n = 30 with no heart disease, n = 30 with LVH, n = 61 with hypertrophic cardiomyopathy (HCM) and n = 28 with cardiac amyloidosis) undergoing a CMR scan were included in this study. We extracted a total of 850 radiomic features and explored their value in disease classification. We applied principal component analysis and unsupervised clustering in exploratory analysis, and then machine learning for feature selection of the best radiomic features that maximized the diagnostic value for cardiac disease classification. The first three principal components of the T1 radiomics were distinctively correlated with cardiac disease type. Unsupervised hierarchical clustering of the population by myocardial T1 radiomics was significantly associated with myocardial disease type (chi
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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.