AIMC Topic: Pericardium

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AI-Driven segmentation and morphogeometric profiling of epicardial adipose tissue in type 2 diabetes.

Cardiovascular diabetology Jul 18, 2025
BACKGROUND: Epicardial adipose tissue (EAT) is associated with cardiometabolic risk in type 2 diabetes (T2D), but its spatial distribution and structural alterations remain understudied. We aim to develop a shape-aware, AI-based method for automated ...
Reproducibility of Results Sweden Aged Male Middle Aged Humans Female Predictive Value of Tests Image Interpretation, Computer-Assisted Adiposity Pericardium Epicardial Adipose Tissue Case-Control Studies Deep Learning Diabetes Mellitus, Type 2 Magnetic Resonance Imaging Adipose Tissue
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Poincare guided geometric UNet for left atrial epicardial adipose tissue segmentation in Dixon MRI images.

Scientific reports Jul 15, 2025
Epicardial Adipose Tissue (EAT) is a recognized risk factor for cardiovascular diseases and plays a pivotal role in the pathophysiology of Atrial Fibrillation (AF). Accurate automatic segmentation of the EAT around the Left Atrium (LA) from Magnetic ...
Heart Atria Female Pericardium Aged Epicardial Adipose Tissue Male Humans Middle Aged Imaging, Three-Dimensional Deep Learning Image Processing, Computer-Assisted Magnetic Resonance Imaging Adipose Tissue Atrial Fibrillation
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Epicardial adipose tissue, myocardial remodelling and adverse outcomes in asymptomatic aortic stenosis: a post hoc analysis of a randomised controlled trial.

Heart (British Cardiac Society) Jun 26, 2025
BACKGROUND: Epicardial adipose tissue represents a metabolically active visceral fat depot that is in direct contact with the left ventricular myocardium. While it is associated with coronary artery disease, little is known regarding its role in aort...
Severity of Illness Index Aortic Valve Stenosis Adipose Tissue Ventricular Remodeling Asymptomatic Diseases Male Ventricular Function, Left Echocardiography Middle Aged Disease Progression Computed Tomography Angiography Pericardium Epicardial Adipose Tissue Humans Prospective Studies Female Aged
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Impact of CT reconstruction algorithms on pericoronary and epicardial adipose tissue attenuation.

European journal of radiology Apr 23, 2025
OBJECTIVE: This study aims to investigate the impact of adaptive statistical iterative reconstruction-Veo (ASIR-V) and deep learning image reconstruction (DLIR) algorithms on the quantification of pericoronary adipose tissue (PCAT) and epicardial adi...
Retrospective Studies Tomography, X-Ray Computed Female Aged Male Humans Algorithms Middle Aged Radiographic Image Interpretation, Computer-Assisted Deep Learning Computed Tomography Angiography Adipose Tissue Pericardium Coronary Angiography Epicardial Adipose Tissue
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Prediction of obstructive coronary artery disease using coronary calcification and epicardial adipose tissue assessments from CT calcium scoring scans.

Journal of cardiovascular computed tomography Feb 4, 2025
BACKGROUND: Low-cost/no-cost non-contrast CT calcium scoring (CTCS) exams can provide direct evidence of coronary atherosclerosis. In this study, using features from CTCS images, we developed a novel machine learning model to predict obstructive coro...
Coronary Artery Disease Coronary Vessels Vascular Calcification Computed Tomography Angiography Risk Assessment Pericardium Severity of Illness Index Humans Epicardial Adipose Tissue Adipose Tissue Machine Learning Risk Factors Predictive Value of Tests Decision Support Techniques Aged Coronary Angiography Prognosis Male Female Middle Aged Reproducibility of Results
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Automatic segmentation of pericardial adipose tissue from cardiac MR images via semi-supervised method with difference-guided consistency.

Medical physics Dec 5, 2024
BACKGROUND: Accurate and automatic segmentation of pericardial adipose tissue (PEAT) in cardiac magnetic resonance (MR) images is essential for the diagnosis and treatment of cardiovascular diseases. Precise segmentation is challenging due to high co...
Heart Supervised Machine Learning Automation Pericardium Humans Image Processing, Computer-Assisted Magnetic Resonance Imaging Adipose Tissue
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Fully automated epicardial adipose tissue volume quantification with deep learning and relationship with CAC score and micro/macrovascular complications in people living with type 2 diabetes: the multicenter EPIDIAB study.

Cardiovascular diabetology Sep 3, 2024
BACKGROUND: The aim of this study (EPIDIAB) was to assess the relationship between epicardial adipose tissue (EAT) and the micro and macrovascular complications (MVC) of type 2 diabetes (T2D).
Middle Aged Risk Assessment Deep Learning Female Humans Epicardial Adipose Tissue Diabetes Mellitus, Type 2 Predictive Value of Tests Reproducibility of Results Automation Prognosis Adipose Tissue Coronary Angiography Risk Factors Diabetic Angiopathies Aged Vascular Calcification Radiographic Image Interpretation, Computer-Assisted Male Computed Tomography Angiography Coronary Artery Disease Pericardium Adiposity
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DEep LearnIng-based QuaNtification of epicardial adipose tissue predicts MACE in patients undergoing stress CMR.

Atherosclerosis Apr 18, 2024
BACKGROUND AND AIMS: This study investigated the additional prognostic value of epicardial adipose tissue (EAT) volume for major adverse cardiovascular events (MACE) in patients undergoing stress cardiac magnetic resonance (CMR) imaging.
Reproducibility of Results Risk Factors Magnetic Resonance Imaging, Cine Aged Epicardial Adipose Tissue Male Adipose Tissue Stroke Volume Predictive Value of Tests Humans Middle Aged Ventricular Function, Left Coronary Artery Disease Prognosis Risk Assessment Myocardial Infarction Retrospective Studies Deep Learning Pericardium Female Magnetic Resonance Imaging
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Deep learning-based workflow for automatic extraction of atria and epicardial adipose tissue on cardiac computed tomography in atrial fibrillation.

Journal of the Chinese Medical Association : JCMA Feb 21, 2024
BACKGROUND: Preoperative estimation of the volume of the left atrium (LA) and epicardial adipose tissue (EAT) on computed tomography (CT) images is associated with an increased risk of atrial fibrillation (AF) recurrence. We aimed to design a deep le...
Humans Male Heart Atria Tomography, X-Ray Computed Middle Aged Pericardium Female Deep Learning Epicardial Adipose Tissue Aged Workflow Atrial Fibrillation
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Automated assessment of cardiac pathologies on cardiac MRI using T1-mapping and late gadolinium phase sensitive inversion recovery sequences with deep learning.

BMC medical imaging Feb 13, 2024
BACKGROUND: A deep learning (DL) model that automatically detects cardiac pathologies on cardiac MRI may help streamline the diagnostic workflow. To develop a DL model to detect cardiac pathologies on cardiac MRI T1-mapping and late gadolinium phase ...
Humans Deep Learning Contrast Media Magnetic Resonance Imaging Myocardium Gadolinium Pericardium
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