ArticleAdvanced science (Weinheim, Baden-Wurttemberg, Germany)2025
Non-Invasive Diagnosis of Chronic Myocardial Infarction via Composite In-Silico-Human Data Learning.
Article in Advanced science (Weinheim, Baden-Wurttemberg, Germany), 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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Who cites it
10 citing papers in PubMed.
- Computational modeling of left ventricular flow using PC-CMR-derived four-dimensional wall motion.International journal of engineering science · 2026Article
- Cardiovascular Imaging for the Early Detection of Cardiotoxicity from Emerging Cancer Therapies: Mechanistic Insights Across the Pediatric and Adult Spectrum.Journal of personalized medicine · 2026Review
- An AI-enabled tool for quantifying overlapping red blood cell sickling dynamics in microfluidic assays.Lab on a chip · 2026Article
- Advancing cardiovascular disease diagnosis with an interpretable and responsible AI framework.Scientific reports · 2026Article
- Towards fully automated synthetic ECV quantification: an open-access machine learning-based approach for fast blood draw-free CMR.Scientific reports · 2026Article
- A practical ECG-based model for early identification of acute heart failure following acute myocardial infarction.Scientific reports · 2026Article
- Enhancing MRI contrast between sub-chronic myocardial infarct, remote and healthy myocardium using periodic irradiation in the rotating frames.Scientific reports · 2026Article
- Patient-specific hemodynamic simulation for left ventricular assist device via non-invasive monitoring based on lumped parameter model and hierarchical neural network.Frontiers in physiology · 2026Article
- Non-Invasive Diagnosis of Chronic Myocardial Infarction via Composite In-Silico-Human Data Learning.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2025Article
- Enhancing cardiac disease detection via a fusion of machine learning and medical imaging.Scientific reports · 2025Article
Corrections and comments
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Authors and funding
8 authors.
Funding
Abstract
Myocardial infarction (MI) continues to be a leading cause of death worldwide. The precise quantification of infarcted tissue is crucial to diagnosis, therapeutic management, and post-MI care. Late gadolinium enhancement-cardiac magnetic resonance (LGE-CMR) is regarded as the gold standard for precise infarct tissue localization in MI patients. A fundamental limitation of LGE-CMR is the invasive intravenous introduction of gadolinium-based contrast agents that present potential high-risk toxicity, particularly for individuals with underlying chronic kidney diseases. Herein, a completely non-invasive methodology is developed to identify the location and extent of an infarct region in the left ventricle via a machine learning (ML) model using only cardiac strains as inputs. In this approach, the remarkable performance of a multi-fidelity ML model is demonstrated, which combines rodent-based in-silico-generated training data (low-fidelity) with very limited patient-specific human data (high-fidelity) in predicting LGE ground truth. The results offer a new paradigm for developing feasible prognostic tools by augmenting synthetic simulation-based data with very small amounts of in vivo human data. More broadly, the proposed approach can significantly assist with addressing biomedical challenges in healthcare where human data are limited.
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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 OpenQuestion graph.