AIMC Topic: Pericardium

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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...
Humans Machine Learning Mass Screening Adipose Tissue Image Interpretation, Computer-Assisted Pericardium Software Female Male Middle Aged
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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...
Female Aged Aged, 80 and over Male Humans Prospective Studies Prognosis Tomography, X-Ray Computed Cardiometabolic Risk Factors Predictive Value of Tests Deep Learning Radiographic Image Interpretation, Computer-Assisted Los Angeles Time Factors Registries Middle Aged Metabolic Syndrome Heart Diseases Non-alcoholic Fatty Liver Disease Risk Assessment Adipose Tissue Adiposity Prevalence Pericardium
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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...
Image Processing, Computer-Assisted Magnetic Resonance Imaging Heart Ventricles Pericardium Neural Networks, Computer Algorithms
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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...
Pericardium Deep Learning Adipose Tissue Predictive Value of Tests Coronary Angiography Coronary Artery Disease Coronary Vessels Vascular Calcification Asymptomatic Diseases Aged, 80 and over Male Middle Aged Humans Risk Assessment Prognosis Female Tomography, X-Ray Computed Risk Factors Aged
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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...
Tomography, X-Ray Computed Sensitivity and Specificity Image Processing, Computer-Assisted Adipose Tissue Pericardium Humans Neural Networks, Computer Algorithms
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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...
Humans Tomography, X-Ray Computed Female Aged Male Middle Aged Deep Learning Pericardium Image Processing, Computer-Assisted Adipose Tissue Thorax
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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...
Pericardium Anisotropy Collagen Stress, Mechanical Elasticity Glutaral Microscopy, Confocal Regression Analysis Nonlinear Dynamics Machine Learning Animals Cattle ROC Curve Neural Networks, Computer
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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...
Tomography, X-Ray Computed Female Male Image Processing, Computer-Assisted Humans Machine Learning Adipose Tissue Pericardium Mediastinum
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Epicardial adipose tissue and myocardial ischemia assessed by computed tomography perfusion imaging and invasive fractional flow reserve.

Journal of cardiovascular computed tomography Dec 30, 2016
BACKGROUND: Epicardial adipose tissue (EAT) is a metabolically active fat depot that is associated with incident coronary artery disease (CAD) and major adverse cardiovascular events. The relationship between EAT and myocardial ischemia remains uncle...
Pericardium Predictive Value of Tests Coronary Angiography Radiographic Image Interpretation, Computer-Assisted Coronary Artery Disease Coronary Vessels Myocardial Perfusion Imaging Coronary Stenosis Fractional Flow Reserve, Myocardial Aged Male Middle Aged Adipose Tissue Humans Tomography, X-Ray Computed Female
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Automatic Deep Learning Segmentation and Quantification of Epicardial Adipose Tissue in Non-Contrast Cardiac CT scans.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference Nov 1, 2021
An Automatic deep learning semantic segmentation (ADLS) using DeepLab-v3-plus technique is proposed for a full and accurate whole heart Epicardial adipose tissue (EAT) segmentation from non-contrast cardiac CT scan. The ADLS algorithm was trained on ...
Thorax Pericardium Tomography, X-Ray Computed Deep Learning Adipose Tissue
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