ArticleNPJ digital medicine2024
AI-derived epicardial fat measurements improve cardiovascular risk prediction from myocardial perfusion imaging.
Article in NPJ digital medicine, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 26 papers.
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Who cites it
26 citing papers in PubMed.
- Beyond BMI: an interpretable integrated body composition index from low-dose chest CT for all-cause mortality risk stratification: a multicentre study.medRxiv : the preprint server for health sciences · 2026Article
- Revisiting Perfusion in the Forgotten Right Ventricle: Artificial Intelligence-Enhanced Quantification on PET/CT.Journal of nuclear medicine : official publication, Society of Nuclear Medicine · 2026Article
- Molecular Basis of Adipose-Cardiac Crosstalk in Cardiovascular Diseases: From Mechanisms to Therapeutic Opportunities.Biomolecules · 2026Review
- Role of systemic and epicardial adipose tissue in cardiometabolic disease.Nature reviews. Cardiology · 2026Review
- CT Attenuation Map-Derived Body Composition Is Associated with Cardiorespiratory Fitness in Multicenter External Validation.medRxiv : the preprint server for health sciences · 2026Article
- Feasibility of Artificial Intelligence Models for Longitudinal CT Analysis of Epicardial Adipose Tissue After Immunotherapy.Diagnostics (Basel, Switzerland) · 2026Article
- Sex-specific cardiovascular risk prediction using AI-derived epicardial adipose tissue measurements on CT calcium scoring exams.American journal of preventive cardiology · 2026Article
- Coronary CT angiography evaluation with artificial intelligence for individualized medical treatment of atherosclerosis: a Consensus Statement from the QCI Study Group.Nature reviews. Cardiology · 2026Review
- Automated assessment of breast arterial calcification: a new frontier in cardiovascular risk prediction.Frontiers in cardiovascular medicine · 2026Review
- Cardiac CT for personalized phenotyping in stable coronary artery disease: toward precision medicine.BJR open · 2026Review
- Ejection fraction quantification from ungated chest CT by AI.medRxiv : the preprint server for health sciences · 2025Article
- Cardiac CT in the era of artificial intelligence: precision imaging, treatment guidance and optimised risk stratification for coronary artery disease.Open heart · 2025Review
- Deep learning-based quantification of epicardial adipose tissue volume from non-contrast computed tomography images: a multi-centre study.European heart journal. Digital health · 2025Article
- AI in Adipose Imaging: Revolutionizing Visceral Adipose Tissue, Ectopic Fat, and Cardiovascular Risk Assessment.Current atherosclerosis reports · 2025Review
- Novel Strategies to Conquer Residual Adiposity Risk in Cardiovascular Disease.Current atherosclerosis reports · 2025Review
- Beyond Traditional Risk Factors: Integrating Epicardial Adipose Tissue into the Comorbidity Landscape of HFpEF.Journal of clinical medicine · 2025Review
- Deep learning-quantified body composition from positron emission tomography/computed tomography and cardiovascular outcomes: a multicentre study.European heart journal · 2025Article
- AI-based volumetric six-tissue body composition quantification from CT cardiac attenuation scans for mortality prediction: a multicentre study.The Lancet. Digital health · 2025Article
- Holistic AI analysis of hybrid cardiac perfusion images for mortality prediction.NPJ digital medicine · 2025Article
- Non-invasive Assessment of Coronary Artery Disease: The Role of AI in the Current Status and Future Directions.Cureus · 2025Review
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Authors and funding
16 authors.
Funding
Abstract
Epicardial adipose tissue (EAT) volume and attenuation are associated with cardiovascular risk, but manual annotation is time-consuming. We evaluated whether automated deep learning-based EAT measurements from ungated computed tomography (CT) are associated with death or myocardial infarction (MI). We included 8781 patients from 4 sites without known coronary artery disease who underwent hybrid myocardial perfusion imaging. Of those, 500 patients from one site were used for model training and validation, with the remaining patients held out for testing (n = 3511 internal testing, n = 4770 external testing). We modified an existing deep learning model to first identify the cardiac silhouette, then automatically segment EAT based on attenuation thresholds. Deep learning EAT measurements were obtained in <2 s compared to 15 min for expert annotations. There was excellent agreement between EAT attenuation (Spearman correlation 0.90 internal, 0.82 external) and volume (Spearman correlation 0.90 internal, 0.91 external) by deep learning and expert segmentation in all 3 sites (Spearman correlation 0.90-0.98). During median follow-up of 2.7 years (IQR 1.6-4.9), 565 patients experienced death or MI. Elevated EAT volume and attenuation were independently associated with an increased risk of death or MI after adjustment for relevant confounders. Deep learning can automatically measure EAT volume and attenuation from low-dose, ungated CT with excellent correlation with expert annotations, but in a fraction of the time. EAT measurements offer additional prognostic insights within the context of hybrid perfusion imaging.
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Registered trials
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