ReviewQuantitative imaging in medicine and surgery2022
Epicardial and pericardial fat analysis on CT images and artificial intelligence: a literature review.
Review in Quantitative imaging in medicine and surgery, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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.
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Who cites it
21 citing papers in PubMed.
- Role of systemic and epicardial adipose tissue in cardiometabolic disease.Nature reviews. Cardiology · 2026Review
- Feasibility of Artificial Intelligence Models for Longitudinal CT Analysis of Epicardial Adipose Tissue After Immunotherapy.Diagnostics (Basel, Switzerland) · 2026Article
- Validation of a deep learning approach for epicardial adipose tissue segmentation in computed tomography.The international journal of cardiovascular imaging · 2026Article
- Epicardial adipose tissue in coronary microvascular disease.American heart journal plus : cardiology research and practice · 2026Review
- Novel Strategies to Conquer Residual Adiposity Risk in Cardiovascular Disease.Current atherosclerosis reports · 2025Review
- Association of liver multi-parameter quantitative metrics determined by dual-layer spectral detector computed tomography (SDCT) with coronary plaque scores.Quantitative imaging in medicine and surgery · 2024Article
- Relationship between different clinical characteristics and pericoronary adipose tissue attenuation values quantified from coronary computed tomographic angiography (CCTA) in patients without coronary heart disease (CHD).Quantitative imaging in medicine and surgery · 2024Article
- Visceral adiposity in patients with lipomatous hypertrophy of the interatrial septum.Heart and vessels · 2024Article
- Emerging technologies in adipose tissue research.Adipocyte · 2023Review
- Automatic epicardial adipose tissue segmentation in pulmonary computed tomography venography using nnU-Net.Quantitative imaging in medicine and surgery · 2023Article
- Computed tomography angiography-based radiomics model to identify high-risk carotid plaques.Quantitative imaging in medicine and surgery · 2023Article
- Proceedings of the NHLBI Workshop on Artificial Intelligence in Cardiovascular Imaging: Translation to Patient Care.JACC. Cardiovascular imaging · 2023Review
- Role of Cardiovascular Imaging in Risk Assessment: Recent Advances, Gaps in Evidence, and Future Directions.Journal of clinical medicine · 2023Review
- Impact of Quantitative Computed Tomography-Based Analysis of Abdominal Adipose Tissue in Patients with Lymphoma.Hematology reports · 2023Article
- Artificial intelligence and obesity management: An Obesity Medicine Association (OMA) Clinical Practice Statement (CPS) 2023.Obesity pillars · 2023Article
- Association of epicardial adipose tissue volume with increased risk of hemodynamically significant coronary artery disease.Quantitative imaging in medicine and surgery · 2023Article
- Cardiac Computed Tomography Evaluation of Association of Left Ventricle Disfunction and Epicardial Adipose Tissue Density in Patients with Low to Intermediate Cardiovascular Risk.Medicina (Kaunas, Lithuania) · 2023Article
- Artificial intelligence in coronary computed tomography angiography: Demands and solutions from a clinical perspective.Frontiers in cardiovascular medicine · 2023Review
- Machine learning of adipose tissue in atrial fibrillation.Heart rhythm · 2022Article
- Assessment of elastographic Q-analysis score combined with Prostate Imaging-Reporting and Data System (PI-RADS) based on transrectal ultrasound (TRUS)/multi-parameter magnetic resonance imaging (MP-MRI) fusion-guided biopsy in differentiating benign and malignant prostate.Quantitative imaging in medicine and surgery · 2022Article
Corrections and comments
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Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The present review summarizes the available evidence on artificial intelligence (AI) algorithms aimed to the segmentation of epicardial and pericardial adipose tissues on computed tomography (CT) images. Body composition imaging is a novel concept based on quantitative analysis of body tissues. Manual segmentation of medical images allows to obtain quantitative and qualitative data on several tissues including epicardial and pericardial fat. However, since manual segmentation requires a considerable amount of time, the analysis of adipose tissue compartments based on AI has been proposed as an automatic, reliable, accurate and fast tool. The literature research was performed on March 2021 using MEDLINE PubMed Central and "adipose tissue artificial intelligence", "adipose tissue deep learning" or "adipose tissue machine learning" as keywords for articles search. Relevant articles concerning epicardial adipose tissue, pericardial adipose tissue and AI were selected. The evaluation of adipose tissue compartments can provide additional information on the pathogenesis and prognosis of several diseases, including cardiovascular. AI can assist physicians to obtain important information, possibly improving the patient's quality of life and identifying patients at risk of developing variable disorders.
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What Socratic holds
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 Socratic graph.