ArticleMorphologie : bulletin de l'Association des anatomistes2019
Improving the clinical understanding of hypertrophic cardiomyopathy by combining patient data, machine learning and computer simulations: A case study.
Article in Morphologie : bulletin de l'Association des anatomistes, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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Who cites it
9 citing papers in PubMed.
- Artificial intelligence applications in hypertrophic cardiomyopathy: turns and loopholes.European heart journal. Digital health · 2025Review
- Computational modelling of biological systems now and then: revisiting tools and visions from the beginning of the century.Philosophical transactions. Series A, Mathematical, physical, and engineering sciences · 2025Review
- Computational Medicine: What Electrophysiologists Should Know to Stay Ahead of the Curve.Current cardiology reports · 2024Review
- Computational modeling of cardiac electrophysiology and arrhythmogenesis: toward clinical translation.Physiological reviews · 2024Review
- Disease Progression of Hypertrophic Cardiomyopathy: Modeling Using Machine Learning.JMIR medical informatics · 2022Article
- A Two-Stage Model for Predicting Mild Cognitive Impairment to Alzheimer's Disease Conversion.Frontiers in aging neuroscience · 2022Article
- Basic Research Approaches to Evaluate Cardiac Arrhythmia in Heart Failure and Beyond.Frontiers in physiology · 2022Review
- Whole-heart ventricular arrhythmia modeling moving forward: Mechanistic insights and translational applications.Biophysics reviews · 2021Article
- Precision medicine in human heart modeling : Perspectives, challenges, and opportunities.Biomechanics and modeling in mechanobiology · 2021Review
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Authors and funding
4 authors.
Funding
Abstract
Most patients with hypertrophic cardiomyopathy (HCM), the most common genetic cardiac disease, remain asymptomatic, but others may suffer from sudden cardiac death. A better identification of those patients at risk, together with a better understanding of the mechanisms leading to arrhythmia, are crucial to target high-risk patients and provide them with appropriate treatment. However, this currently remains a challenge. In this paper, we present a successful example of implementing computational techniques for clinically-relevant applications. By combining electrocardiogram and imaging data, machine learning and high performance computing simulations, we identified four phenotypes in HCM, with differences in arrhythmic risk, and provided two distinct possible mechanisms that may explain the heterogeneity of HCM manifestation. This led to a better HCM patient stratification and understanding of the underlying disease mechanisms, providing a step further towards tailored HCM patient management and treatment.
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Registered trials
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