ArticleJACC. Cardiovascular imaging2023
Deep-Learning for Epicardial Adipose Tissue Assessment With Computed Tomography: Implications for Cardiovascular Risk Prediction.
Article in JACC. Cardiovascular imaging, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 56 papers, 2 of them syntheses that pooled it.
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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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
56 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- [Recent Advances in the Interorgan Regulatory Roles of Adipose Tissue].Sichuan da xue xue bao. Yi xue ban = Journal of Sichuan University. Medical science edition · 2026Pooled it
- Elevated Epicardial Adipose Tissue and Ischemic Stroke Risk: A Systematic Review and Meta-Analysis.Medicina (Kaunas, Lithuania) · 2025Pooled it
- Effects of breaking up prolonged sitting via exercise snacks intervention on the body composition and plasma metabolomics of sedentary obese adults: a randomized controlled trial.Endocrine journal · 2025Trial
- AI-Enabled Echocardiography Identifies Prominent Epicardial Adipose Tissue Associated With an Adverse Cardiometabolic Profile.medRxiv : the preprint server for health sciences · 2026Article
- Epicardial and pericardial adipose tissue: anatomy, physiology, imaging, segmentation, and treatment effects.The British journal of radiology · 2026Review
- Epicardial Adipose Tissue Shows No Significant Incremental Value for Discriminating Coronary Artery Disease in Adults Aged 80 Years and Older: A Machine Learning Analysis.Journal of cardiovascular development and disease · 2026Article
- Molecular Basis of Adipose-Cardiac Crosstalk in Cardiovascular Diseases: From Mechanisms to Therapeutic Opportunities.Biomolecules · 2026Review
- Early Prediction of Heart Failure From Routine Cardiac CT Using Radiomic Phenotyping of Epicardial Fat.Journal of the American College of Cardiology · 2026Article
- Role of systemic and epicardial adipose tissue in cardiometabolic disease.Nature reviews. Cardiology · 2026Review
- Epicardial Adipose Tissue CT Radiomics Improves Acute Coronary Syndrome Prediction Beyond Coronary Artery Calcium Score.Diagnostics (Basel, Switzerland) · 2026Article
- Review
- Relationships between three-dimensional fibrosis distribution, atrial adiposity, and voltage abnormalities associated with persistent atrial fibrillation.Europace : European pacing, arrhythmias, and cardiac electrophysiology : journal of the working groups on cardiac pacing, arrhythmias, and cardiac cellular electrophysiology of the European Society of Cardiology · 2026Article
- Review
- Clinical Applications of Artificial Intelligence in Cardiovascular Imaging: Where Do We Stand?Life (Basel, Switzerland) · 2026Review
- Epicardial Adipose Tissue as a Cardiometabolic Target in Atrial Fibrillation: Implications for Ablation Strategies and Emerging Metabolic Therapies.Medical sciences (Basel, Switzerland) · 2026Review
- Epicardial adipose tissue volume outperforms density in association with cardiorenal complications in hypertensive patients.Lipids in health and disease · 2026Article
- Research on epicardial adipose tissue as a metabolic therapeutic target in AF: focus on GLP-1 receptor agonists.Frontiers in cardiovascular medicine · 2026Review
- Cardiac CT for personalized phenotyping in stable coronary artery disease: toward precision medicine.BJR open · 2026Review
- Emerging quantitative CCTA imaging biomarkers for cardiovascular risk stratification: a narrative review.Frontiers in cardiovascular medicine · 2026Review
- Macrophage Immunometabolism in Epicardial Adipose Tissue and Obesity-Related Atrial Fibrillation.Journal of inflammation research · 2026Review
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25 authors.
Funding
Abstract
backgroundEpicardial adipose tissue (EAT) volume is a marker of visceral obesity that can be measured in coronary computed tomography angiograms (CCTA). The clinical value of integrating this measurement in routine CCTA interpretation has not been documented.
objectivesThis study sought to develop a deep-learning network for automated quantification of EAT volume from CCTA, test it in patients who are technically challenging, and validate its prognostic value in routine clinical care.
methodsThe deep-learning network was trained and validated to autosegment EAT volume in 3,720 CCTA scans from the ORFAN (Oxford Risk Factors and Noninvasive Imaging Study) cohort. The model was tested in patients with challenging anatomy and scan artifacts and applied to a longitudinal cohort of 253 patients post-cardiac surgery and 1,558 patients from the SCOT-HEART (Scottish Computed Tomography of the Heart) Trial, to investigate its prognostic value.
resultsExternal validation of the deep-learning network yielded a concordance correlation coefficient of 0.970 for machine vs human. EAT volume was associated with coronary artery disease (odds ratio [OR] per SD increase in EAT volume: 1.13 [95% CI: 1.04-1.30]; P = 0.01), and atrial fibrillation (OR: 1.25 [95% CI: 1.08-1.40]; P = 0.03), after correction for risk factors (including body mass index). EAT volume predicted all-cause mortality (HR per SD: 1.28 [95% CI: 1.10-1.37]; P = 0.02), myocardial infarction (HR: 1.26 [95% CI:1.09-1.38]; P = 0.001), and stroke (HR: 1.20 [95% CI: 1.09-1.38]; P = 0.02) independently of risk factors in SCOT-HEART (5-year follow-up). It also predicted in-hospital (HR: 2.67 [95% CI: 1.26-3.73]; P ≤ 0.01) and long-term post-cardiac surgery atrial fibrillation (7-year follow-up; HR: 2.14 [95% CI: 1.19-2.97]; P ≤ 0.01).
conclusionsAutomated assessment of EAT volume is possible in CCTA, including in patients who are technically challenging; it forms a powerful marker of metabolically unhealthy visceral obesity, which could be used for cardiovascular risk stratification.
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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.