ArticleEuropean heart journal. Cardiovascular Imaging2022
Machine learning phenotyping of scarred myocardium from cine in hypertrophic cardiomyopathy.
Article in European heart journal. Cardiovascular Imaging, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 26 papers.
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Who cites it
26 citing papers in PubMed, 37 citations in OpenAlex.
- AI-Powered Detection of Left Ventricular Myocardial Scar from Dual-Sequence CT: Global and Segmental Prediction Validated by CMR.Journal of imaging informatics in medicine · 2026Article
- Myocardial Fibrosis in Hypertrophic Cardiomyopathy: Leveraging Quantitative Imaging and Artificial Intelligence for Precision Management.Reviews in cardiovascular medicine · 2026Review
- A generalizable deep learning system for cardiac MRI.Nature biomedical engineering · 2026Article
- Digital twins in hypertrophic cardiomyopathy from sarcomeric mutation to predictive personalized care.Annals of medicine and surgery (2012) · 2026Article
- Cardiac left ventricular MRI texture analysis to derive texture characteristics of a healthy population: clinical implications.European radiology · 2026Article
- Promises and challenges of AI-enabled methods for myocardial characterisation in cardiovascular magnetic resonance.Frontiers in cardiovascular medicine · 2026Review
- Myocardial radiomics of non-ischemic cardiomyopathy using cardiovascular magnetic resonance: current perspectives and future directions.Frontiers in cardiovascular medicine · 2026Review
- Differentiation of light chain cardiac amyloidosis and hypertrophic cardiomyopathy by ensemble machine learning-based radiomic analysis of cardiac magnetic resonance.Orphanet journal of rare diseases · 2025Article
- Leveraging artificial intelligence for risk stratification of inherited cardiomyopathies in under-resourced settings.Heart rhythm O2 · 2025Article
- Progress in the Clinical Application of Artificial Intelligence for Left Ventricle Analysis in Cardiac Magnetic Resonance.Reviews in cardiovascular medicine · 2024Review
- [Clincial Research Progress in Using Magnetic Resonance Imaging to Assess Myocardial Fibrosis in Hypertrophic Cardiomyopathy].Sichuan da xue xue bao. Yi xue ban = Journal of Sichuan University. Medical science edition · 2024Review
- The beating heart: artificial intelligence for cardiovascular application in the clinic.Magma (New York, N.Y.) · 2024Review
- Lightweight preprocessing and template matching facilitate streamlined ischemic myocardial scar classification.Journal of medical imaging (Bellingham, Wash.) · 2024Article
- Discrimination models with radiomics features derived from cardiovascular magnetic resonance images for distinguishing hypertensive heart disease from hypertrophic cardiomyopathy.Cardiovascular diagnosis and therapy · 2024Article
- Radiomics from Cardiovascular MR Cine Images for Identifying Patients with Hypertrophic Cardiomyopathy at High Risk for Heart Failure.Radiology. Cardiothoracic imaging · 2024Article
- Association of increased epicardial adipose tissue derived from cardiac magnetic resonance imaging with myocardial fibrosis in Duchenne muscular dystrophy: a clinical prediction model development and validation study in 283 participants.Quantitative imaging in medicine and surgery · 2024Article
- Present and Future Innovations in AI and Cardiac MRI.Radiology · 2024Review
- Gadolinium-Free Cardiac MRI Myocardial Scar Detection by 4D Convolution Factorization.Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention · 2023Article
- Radiomics-Based Quality Control System for Automatic Cardiac Segmentation: A Feasibility Study.Bioengineering (Basel, Switzerland) · 2023Article
- Arrhythmic Risk Stratification among Patients with Hypertrophic Cardiomyopathy.Journal of clinical medicine · 2023Review
Corrections and comments
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Authors and funding
11 authors at 4 institutions in 2 countries.
Funding
Abstract
aimsCardiovascular magnetic resonance (CMR) with late-gadolinium enhancement (LGE) is increasingly being used in hypertrophic cardiomyopathy (HCM) for diagnosis, risk stratification, and monitoring. However, recent data demonstrating brain gadolinium deposits have raised safety concerns. We developed and validated a machine-learning (ML) method that incorporates features extracted from cine to identify HCM patients without fibrosis in whom gadolinium can be avoided. METHODS AND
resultsAn XGBoost ML model was developed using regional wall thickness and thickening, and radiomic features of myocardial signal intensity, texture, size, and shape from cine. A CMR dataset containing 1099 HCM patients collected using 1.5T CMR scanners from different vendors and centres was used for model development (n=882) and validation (n=217). Among the 2613 radiomic features, we identified 7 features that provided best discrimination between +LGE and -LGE using 10-fold stratified cross-validation in the development cohort. Subsequently, an XGBoost model was developed using these radiomic features, regional wall thickness and thickening. In the independent validation cohort, the ML model yielded an area under the curve of 0.83 (95% CI: 0.77-0.89), sensitivity of 91%, specificity of 62%, F1-score of 77%, true negatives rate (TNR) of 34%, and negative predictive value (NPV) of 89%. Optimization for sensitivity provided sensitivity of 96%, F2-score of 83%, TNR of 19% and NPV of 91%; false negatives halved from 4% to 2%.
conclusionAn ML model incorporating novel radiomic markers of myocardium from cine can rule-out myocardial fibrosis in one-third of HCM patients referred for CMR reducing unnecessary gadolinium administration.
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Registered trials
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 Socratic graph.