ArticleDiagnostics (Basel, Switzerland)2022
Deep-Learning Segmentation of Epicardial Adipose Tissue Using Four-Chamber Cardiac Magnetic Resonance Imaging.
Article in Diagnostics (Basel, Switzerland), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
10 citing papers in PubMed.
- Epicardial and pericardial adipose tissue: anatomy, physiology, imaging, segmentation, and treatment effects.The British journal of radiology · 2026Review
- Editorial for "Standardized MRI-Based Quantification of Epicardial Adipose Tissue Volume: Reproducibility and Agreement With Cardiac Computed Tomography".Journal of magnetic resonance imaging : JMRI · 2026Article
- Epicardial and paracardial adipose tissue quantification in short-axis cardiac cine MRI using deep learning.Magma (New York, N.Y.) · 2026Article
- Beyond Traditional Risk Factors: Integrating Epicardial Adipose Tissue into the Comorbidity Landscape of HFpEF.Journal of clinical medicine · 2025Review
- AI-Driven segmentation and morphogeometric profiling of epicardial adipose tissue in type 2 diabetes.Cardiovascular diabetology · 2025Article
- Poincare guided geometric UNet for left atrial epicardial adipose tissue segmentation in Dixon MRI images.Scientific reports · 2025Article
- Sex-specific Associations of Aldosterone and Renin With Body Composition: A Population-based Cohort Study.The Journal of clinical endocrinology and metabolism · 2025Article
- Trajectory correction enables free-running chemical shift encoded imaging for accurate cardiac proton-density fat fraction quantification at 3T.Journal of cardiovascular magnetic resonance : official journal of the Society for Cardiovascular Magnetic Resonance · 2024 · on this mapArticle
- Role of epicardial adipose tissue in diabetic cardiomyopathy through the lens of cardiovascular magnetic resonance imaging - a narrative review.Therapeutic advances in endocrinology and metabolism · 2024Review
- The Role and Implications of Epicardial Fat in Coronary Atherosclerotic Disease.Journal of clinical medicine · 2022Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
9 authors.
Funding
Abstract
In magnetic resonance imaging (MRI), epicardial adipose tissue (EAT) overload remains often overlooked due to tedious manual contouring in images. Automated four-chamber EAT area quantification was proposed, leveraging deep-learning segmentation using multi-frame fully convolutional networks (FCN). The investigation involved 100 subjects-comprising healthy, obese, and diabetic patients-who underwent 3T cardiac cine MRI, optimized U-Net and FCN (noted FCNB) were trained on three consecutive cine frames for segmentation of central frame using dice loss. Networks were trained using 4-fold cross-validation (
Indexed as
Identifiers
What OpenQuestion 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 OpenQuestion graph.