ArticleEBioMedicine2020
A low-cost texture-based pipeline for predicting myocardial tissue remodeling and fibrosis using cardiac ultrasound.
Article in EBioMedicine, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT06062316 (Early Warning Model of Myocardial Remodeling After Acute Myocardial Infarction Based on Multimodal Feature Structure Technology), which is not on this map. Cited by 25 papers.
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The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Early Warning Model of Myocardial Remodeling After Acute Myocardial Infarction Based on Multimodal Feature Structure Technology
Who cites it
25 citing papers in PubMed, 52 citations in OpenAlex.
- Deep Survival Modeling With Echocardiographic Myocardial Texture Radiomics for Prediction of Major Adverse Cardiovascular Events in Hypertrophic Cardiomyopathy.Journal of cardiovascular translational research · 2026Article
- Incremental prognostic value of CCTA-based myocardial radiomics over clinical factors, anatomic and hemodynamic CTA findings in patients with stable angina.The international journal of cardiovascular imaging · 2026Review
- Advances in Non-Invasive Myocardial Stiffness Assessment and Clinical Applications in Hypertrophic Cardiomyopathy.Reviews in cardiovascular medicine · 2026Review
- Integrating preoperative multiregion radiomic features with clinical data to predict atrial fibrillation recurrence after radiofrequency ablation.Frontiers in cardiovascular medicine · 2026Article
- Artificial intelligence in echocardiography: current applications and future perspectives.Journal of echocardiography · 2025Review
- Ultrasonic Texture Analysis for Predicting Acute Myocardial Infarction.JACC. Cardiovascular imaging · 2025Article
- Artificial Intelligence in Clinics: Enhancing Cardiology Practice.JMA journal · 2025Review
- Interpretable machine learning models based on shear-wave elastography radiomics for predicting cardiovascular disease in diabetic kidney disease patients.Journal of diabetes investigation · 2024Article
- Cardiac ultrasomics for acute myocardial infarction risk stratification and prediction of all-cause mortality: a feasibility study.Echo research and practice · 2024Article
- Prediction of heart failure and all-cause mortality using cardiac ultrasomics in patients with breast cancer.The international journal of cardiovascular imaging · 2024Article
- Interpretable machine learning models for predicting skip metastasis in cN0 papillary thyroid cancer based on clinicopathological and elastography radiomics features.Frontiers in oncology · 2024Article
- To predict the left ventricular endocardial scar tissue pattern using Radon descriptor-based machine learning.BMC research notes · 2023Article
- Deep learn-based computer-assisted transthoracic echocardiography: approach to the diagnosis of cardiac amyloidosis.The international journal of cardiovascular imaging · 2023Article
- Topological data analysis in medical imaging: current state of the art.Insights into imaging · 2023Review
- Shear-Wave Elastography-Based Radiomics Nomogram for the Prediction of Cardiovascular Disease in Patients with Diabetic Kidney Disease.Diabetes, metabolic syndrome and obesity : targets and therapy · 2023Article
- Virtual healthcare solutions in heart failure: a literature review.Frontiers in cardiovascular medicine · 2023Review
- Predicting Progression of Kidney Injury Based on Elastography Ultrasound and Radiomics Signatures.Diagnostics (Basel, Switzerland) · 2022Article
- Cardiac microstructural alterations measured by echocardiography identify sex-specific risk for heart failure.Heart (British Cardiac Society) · 2022Article
- Machine learning phenotyping of scarred myocardium from cine in hypertrophic cardiomyopathy.European heart journal. Cardiovascular Imaging · 2022Article
- Echocardiographic Advances in Dilated Cardiomyopathy.Journal of clinical medicine · 2021Review
Corrections and comments
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Authors and funding
8 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundMaturation of ultrasound myocardial tissue characterization may have far-reaching implications as a widely available alternative to cardiac magnetic resonance (CMR) for risk stratification in left ventricular (LV) remodeling.
methodsWe extracted 328 texture-based features of myocardium from still ultrasound images. After we explored the phenotypes of myocardial textures using unsupervised similarity networks, global LV remodeling parameters were predicted using supervised machine learning models. Separately, we also developed supervised models for predicting the presence of myocardial fibrosis using another cohort who underwent cardiac magnetic resonance (CMR). For the prediction, patients were divided into a training and test set (80:20).
findingsTexture-based tissue feature extraction was feasible in 97% of total 534 patients. Interpatient similarity analysis delineated two patient groups based on the texture features: one group had more advanced LV remodeling parameters compared to the other group. Furthermore, this group was associated with a higher incidence of cardiac deaths (p = 0.001) and major adverse cardiac events (p < 0.001). The supervised models predicted reduced LV ejection fraction (<50%) and global longitudinal strain (<16%) with area under the receiver-operator-characteristics curves (ROC AUC) of 0.83 and 0.87 in the hold-out test set, respectively. Furthermore, the presence of myocardial fibrosis was predicted from only ultrasound myocardial texture with an ROC AUC of 0.84 (sensitivity 86.4% and specificity 83.3%) in the test set.
interpretationUltrasound texture-based myocardial tissue characterization identified phenotypic features of LV remodeling from still ultrasound images. Further clinical validation may address critical barriers in the adoption of ultrasound techniques for myocardial tissue characterization.
fundingNone.
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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.