ArticleJournal of cardiovascular magnetic resonance : official journal of the Society for Cardiovascular Magnetic Resonance2022
Radiomics and deep learning for myocardial scar screening in hypertrophic cardiomyopathy.
Article in Journal of cardiovascular magnetic resonance : official journal of the Society for Cardiovascular Magnetic Resonance, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 30 papers.
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Who cites it
30 citing papers in PubMed, 43 citations in OpenAlex.
- Cardiac fibrosis: mechanistic insights and translational advances.Molecular biomedicine · 2026Review
- 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
- Hybrid deep learning time-to-event modeling of major adverse cardiovascular events using coronary artery calcium score scans.European journal of radiology artificial intelligence · 2026Article
- Quantitative imaging analysis in hypertrophic cardiomyopathy: phenotypic differentiation to prognostic stratification.European heart journal. Imaging methods and practice · 2026Review
- Imaging-Based Risk Stratification for Sudden Cardiac Death in Hypertrophic Cardiomyopathy: Current Evidence and Clinical Perspectives.Medical sciences (Basel, Switzerland) · 2026Review
- Clinical Applications of Artificial Intelligence in Cardiovascular Imaging: Where Do We Stand?Life (Basel, Switzerland) · 2026Review
- Artificial Intelligence and Digital Technology in Cardiovascular Imaging: A Narrative Review.Biotech (Basel (Switzerland)) · 2026Review
- Advancements and Applications of Artificial Intelligence in Hypertrophic Cardiomyopathy: A Comprehensive Review.Reviews in cardiovascular medicine · 2026Review
- Digital twins in hypertrophic cardiomyopathy from sarcomeric mutation to predictive personalized care.Annals of medicine and surgery (2012) · 2026Article
- Optimizing imaging techniques in hypertrophic cardiomyopathy: challenges and opportunities.European heart journal. Imaging methods and practice · 2026Review
- Myocardial radiomics of non-ischemic cardiomyopathy using cardiovascular magnetic resonance: current perspectives and future directions.Frontiers in cardiovascular medicine · 2026Review
- Promises and challenges of AI-enabled methods for myocardial characterisation in cardiovascular magnetic resonance.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
- The Role of Cardiac Magnetic Resonance Imaging in the Management of Hypertrophic Cardiomyopathy.Journal of cardiovascular development and disease · 2025Review
- Multiparameter MRI-based model integrating radiomics and deep learning for preoperative staging of laryngeal squamous cell carcinoma.Scientific reports · 2025Article
- Detection of late gadolinium enhancement in patients with hypertrophic cardiomyopathy using machine learning.International journal of cardiology · 2025Article
- Cardiac magnetic resonance research advances in myocardial fibrosis of hypertrophic cardiomyopathy.Frontiers in cardiovascular medicine · 2025Review
- Deep learning approaches for the detection of scar presence from cine cardiac magnetic resonance adding derived parametric images.Medical & biological engineering & computing · 2025Article
- Beyond plaque segmentation: a combined radiomics-deep learning approach for automated CAD-RADS classification.Frontiers in medicine · 2025Article
- [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
Corrections and comments
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Authors and funding
6 authors at 2 institutions in 1 country.
Funding
Abstract
backgroundMyocardial scar burden quantified using late gadolinium enhancement (LGE) cardiovascular magnetic resonance (CMR), has important prognostic value in hypertrophic cardiomyopathy (HCM). However, nearly 50% of HCM patients have no scar but undergo repeated gadolinium-based CMR over their life span. We sought to develop an artificial intelligence (AI)-based screening model using radiomics and deep learning (DL) features extracted from balanced steady state free precession (bSSFP) cine sequences to identify HCM patients without scar.
methodsWe evaluated three AI-based screening models using bSSFP cine image features extracted by radiomics, DL, or combined DL-Radiomics. Images for 759 HCM patients (50 ± 16 years, 66% men) in a multi-center/vendor study were used to develop and test model performance. An external dataset of 100 HCM patients (53 ± 14 years, 70% men) was used to assess model generalizability. Model performance was evaluated using area-under-receiver-operating curve (AUC).
resultsThe DL-Radiomics model demonstrated higher AUC compared to DL and Radiomics in the internal (0.83 vs 0.77, p = 0.006 and 0.78, p = 0.05; n = 159) and external (0.74 vs 0.64, p = 0.006 and 0.71, p = 0.27; n = 100) datasets. The DL-Radiomics model correctly identified 43% and 28% of patients without scar in the internal and external datasets compared to 42% and 16% by Radiomics model and 42% and 23% by DL model, respectively.
conclusionsA DL-Radiomics AI model using bSSFP cine images outperforms DL or Radiomics models alone as a scar screening tool prior to gadolinium administration. Despite its potential, the clinical utility of the model remains limited and further investigation is needed to improve the accuracy and generalizability.
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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.