AIMC Topic: Pericardium
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Deep-Learning for Epicardial Adipose Tissue Assessment With Computed Tomography: Implications for Cardiovascular Risk Prediction.
JACC. Cardiovascular imaging
Feb 8, 2023
BACKGROUND: Epicardial 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 ...
Deep learning segmentation and quantification method for assessing epicardial adipose tissue in CT calcium score scans.
Scientific reports
Feb 10, 2022
Epicardial adipose tissue volume (EAT) has been linked to coronary artery disease and the risk of major adverse cardiac events. As manual quantification of EAT is time-consuming, requires specialized training, and is prone to human error, we develope...
Artificial intelligence based automatic quantification of epicardial adipose tissue suitable for large scale population studies.
Scientific reports
Dec 13, 2021
To develop a fully automatic model capable of reliably quantifying epicardial adipose tissue (EAT) volumes and attenuation in large scale population studies to investigate their relation to markers of cardiometabolic risk. Non-contrast cardiac CT ima...
Metabolic syndrome, fatty liver, and artificial intelligence-based epicardial adipose tissue measures predict long-term risk of cardiac events: a prospective study.
Cardiovascular diabetology
Jan 29, 2021
BACKGROUND: We sought to evaluate the association of metabolic syndrome (MetS) and computed tomography (CT)-derived cardiometabolic biomarkers (non-alcoholic fatty liver disease [NAFLD] and epicardial adipose tissue [EAT] measures) with long-term ris...
Adipose Tissue
Predictive Value of Tests
Radiographic Image Interpretation, Computer-Assisted
Registries
Prevalence
Deep Learning
Time Factors
Metabolic Syndrome
Pericardium
Cardiometabolic Risk Factors
Non-alcoholic Fatty Liver Disease
Los Angeles
Heart Diseases
Adiposity
Prognosis
Tomography, X-Ray Computed
Female
Aged
Aged, 80 and over
Male
Middle Aged
Risk Assessment
Humans
Prospective Studies
Automatic left ventricle segmentation in short-axis MRI using deep convolutional neural networks and central-line guided level set approach.
Computers in biology and medicine
Jun 23, 2020
In the clinical diagnosis of cardiovascular diseases, left ventricle (LV) segmentation in cardiac magnetic resonance images (MRI) is an indispensable procedure for doctors. To reduce the time needed for diagnosis, we develop an automatic LV segmentat...
Deep Learning-Based Quantification of Epicardial Adipose Tissue Volume and Attenuation Predicts Major Adverse Cardiovascular Events in Asymptomatic Subjects.
Circulation. Cardiovascular imaging
Feb 17, 2020
BACKGROUND: Epicardial adipose tissue (EAT) volume (cm) and attenuation (Hounsfield units) may predict major adverse cardiovascular events (MACE). We aimed to evaluate the prognostic value of fully automated deep learning-based EAT volume and attenua...
Adaptive Fruitfly Based Modified Region Growing Algorithm for Cardiac Fat Segmentation Using Optimal Neural Network.
Journal of medical systems
Mar 15, 2019
Epicardial adipose tissue is a visceral fat that has remained an entity of concern for decades owing to its high correlation with coronary heart disease. It continues to stump medical practitioners on the pretext of its relevance with pericardial fat...
Deep Learning for Quantification of Epicardial and Thoracic Adipose Tissue From Non-Contrast CT.
IEEE transactions on medical imaging
Feb 9, 2018
Epicardial adipose tissue (EAT) is a visceral fat deposit related to coronary artery disease. Fully automated quantification of EAT volume in clinical routine could be a timesaving and reliable tool for cardiovascular risk assessment. We propose a ne...
A deep learning approach to estimate chemically-treated collagenous tissue nonlinear anisotropic stress-strain responses from microscopy images.
Acta biomaterialia
Sep 20, 2017
UNLABELLED: Biological collagenous tissues comprised of networks of collagen fibers are suitable for a broad spectrum of medical applications owing to their attractive mechanical properties. In this study, we developed a noninvasive approach to estim...
Machine learning in the prediction of cardiac epicardial and mediastinal fat volumes.
Computers in biology and medicine
Feb 24, 2017
We propose a methodology to predict the cardiac epicardial and mediastinal fat volumes in computed tomography images using regression algorithms. The obtained results indicate that it is feasible to predict these fats with a high degree of correlatio...
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