ArticleNature cardiovascular research2025
Multimodal AI to forecast arrhythmic death in hypertrophic cardiomyopathy.
Article in Nature cardiovascular research, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 22 papers.
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Who cites it
22 citing papers in PubMed.
- Aortic valve disease: Novel animal models for advancing our understanding of the underlying mechanisms.Animal models and experimental medicine · 2026Review
- Ultrasound Radiomics for Hashimoto's Thyroiditis: State of the Art and Future Perspectives.Journal of clinical ultrasound : JCU · 2026Review
- Machine learning algorithms for predicting arrhythmic events in Hypertrophic Cardiomyopathy: limited enhancement beyond late gadolinium enhancement.The international journal of cardiovascular imaging · 2026Article
- Imaging-anchored multiomics in cardiovascular disease: integrating cardiac imaging, bulk, single-cell, and spatial transcriptomics.Briefings in bioinformatics · 2026Review
- Prediction of appropriate implantable cardioverter-defibrillator therapy using machine learning and routinely available clinical data.European heart journal. Digital health · 2026Article
- From Molecules to Machines: An Integrative Framework Linking Molecular Pathogenesis, Multi-Factorial Risk, Risk Stratification, Clinical Management, and Artificial Intelligence in QT Prolongation and Sudden Cardiac Death.Clinical cardiology · 2026Review
- Automated Echocardiographic Detection of Congenital Heart Disease Using Artificial Intelligence.Circulation · 2026Article
- Evaluation and management of recent onset cardiomyopathy in the current era of heart failure therapeutics: a clinical consensus statement of the Heart Failure Association of the ESC.ESC heart failure · 2026Review
- 2026 Expert Consensus Recommendations on Hypertrophic Cardiomyopathy: A Report of the Task Force of the Taiwan Society of Cardiology.Acta Cardiologica Sinica · 2026Article
- Lightweight and interpretable edge intelligence AI with intrusion detection for trustworthy cardiac arrhythmia in medical IoT.Scientific reports · 2026Article
- CARDIAC-FM: A Multimodal Foundation Model for Cardiovascular Risk Prediction Using ECG and Cardiac MRI.medRxiv : the preprint server for health sciences · 2026Article
- Reimagining cardiac care with AI, LLMs, blockchain, and metaverse.Global cardiology science & practice · 2026Review
- AI-Enabled Precision Echocardiography: Toward Personalized Cardiovascular Care.Diagnostics (Basel, Switzerland) · 2026Review
- Automated Echocardiographic Detection of Congenital Heart Disease Using Artificial Intelligence.medRxiv : the preprint server for health sciences · 2026Article
- Artificial intelligence-empowered echocardiography: an updated review of clinical management in hypertrophic cardiomyopathy.Frontiers in cardiovascular medicine · 2026Review
- Artificial intelligence readiness and its influencing factors among newly qualified nurses: a cross-sectional study.Frontiers in medicine · 2026Article
- Modifiable lifestyle factors and sudden cardiac death: mechanisms, evidence, and prevention strategies.Frontiers in cardiovascular medicine · 2026Review
- AI approaches for predicting progression to acute coronary syndrome among stable coronary heart disease patients.NPJ cardiovascular health · 2025Article
- Artificial Intelligence for Cardiovascular Care in Action: From Learning to Implementation in Health Systems.JACC. Advances · 2025Review
- Unveiling the Future of Infective Endocarditis Diagnosis: The Transformative Role of Metagenomic Next-Generation Sequencing in Culture-Negative Cases.Journal of epidemiology and global health · 2025Review
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Authors and funding
13 authors.
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Abstract
Sudden cardiac death from ventricular arrhythmias is a leading cause of mortality worldwide. Arrhythmic death prognostication is challenging in patients with hypertrophic cardiomyopathy (HCM), a setting where current clinical guidelines show low performance and inconsistent accuracy. Here, we present a deep learning approach, MAARS (Multimodal Artificial intelligence for ventricular Arrhythmia Risk Stratification), to forecast lethal arrhythmia events in patients with HCM by analyzing multimodal medical data. MAARS' transformer-based neural networks learn from electronic health records, echocardiogram and radiology reports, and contrast-enhanced cardiac magnetic resonance images, the latter being a unique feature of this model. MAARS achieves an area under the curve of 0.89 (95% confidence interval (CI) 0.79-0.94) and 0.81 (95% CI 0.69-0.93) in internal and external cohorts and outperforms current clinical guidelines by 0.27-0.35 (internal) and 0.22-0.30 (external). In contrast to clinical guidelines, it demonstrates fairness across demographic subgroups. We interpret MAARS' predictions on multiple levels to promote artificial intelligence transparency and derive risk factors warranting further investigation.
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