AIMC Topic: Plaque, Atherosclerotic
Clear Filters Showing 61 to 70 of 143 articles
A hybrid deep learning paradigm for carotid plaque tissue characterization and its validation in multicenter cohorts using a supercomputer framework.
Computers in biology and medicine
Dec 13, 2021
BACKGROUND: Early and automated detection of carotid plaques prevents strokes, which are the second leading cause of death worldwide according to the World Health Organization. Artificial intelligence (AI) offers automated solutions for plaque tissue...
Automated deep learning-based paradigm for high-risk plaque detection in B-mode common carotid ultrasound scans: an asymptomatic Japanese cohort study.
International angiology : a journal of the International Union of Angiology
Nov 26, 2021
BACKGROUND: The death due to stroke is caused by embolism of the arteries which is due to the rupture of the atherosclerotic lesions in carotid arteries. The lesion formation is over time, and thus, early screening is recommended for asymptomatic and...
Classification of moving coronary calcified plaques based on motion artifacts using convolutional neural networks: a robotic simulating study on influential factors.
BMC medical imaging
Oct 19, 2021
BACKGROUND: Motion artifacts affect the images of coronary calcified plaques. This study utilized convolutional neural networks (CNNs) to classify the motion-contaminated images of moving coronary calcified plaques and to determine the influential fa...
Deep learning-based carotid plaque vulnerability classification with multicentre contrast-enhanced ultrasound video: a comparative diagnostic study.
BMJ open
Aug 27, 2021
OBJECTIVES: The aim of this study was to evaluate the performance of deep learning-based detection and classification of carotid plaque (DL-DCCP) in carotid plaque contrast-enhanced ultrasound (CEUS).
Deep learning with convolutional neural network for estimation of the characterisation of coronary plaques: Validation using IB-IVUS.
Radiography (London, England : 1995)
Aug 14, 2021
INTRODUCTION: Deep learning approaches have shown high diagnostic performance in image classifications, such as differentiation of malignant tumors and calcified coronary plaque. However, it is unknown whether deep learning is useful for characterizi...
Vesseg: An Open-Source Tool for Deep Learning-Based Atherosclerotic Plaque Quantification in Histopathology Images-Brief Report.
Arteriosclerosis, thrombosis, and vascular biology
Aug 12, 2021
Objective: Manual plaque segmentation in microscopy images is a time-consuming process in atherosclerosis research and potentially subject to unacceptable user-to-user variability and observer bias. We address this by releasing Vesseg a tool that inc...
Plaque, Atherosclerotic
Reproducibility of Results
Arteries
Animals
Male
Predictive Value of Tests
Severity of Illness Index
Atherosclerosis
Staining and Labeling
Diagnosis, Computer-Assisted
Software
Microscopy
Disease Models, Animal
Deep Learning
Female
Image Interpretation, Computer-Assisted
Mice
Vascular Remodeling
Mice, Inbred C57BL
Mice, Knockout, ApoE
Deep Learning-Based Measurement of Total Plaque Area in B-Mode Ultrasound Images.
IEEE journal of biomedical and health informatics
Aug 5, 2021
Measurement of total-plaque-area (TPA) is important for determining long term risk for stroke and monitoring carotid plaque progression. Since delineation of carotid plaques is required, a deep learning method can provide automatic plaque segmentatio...
Hybrid deep learning segmentation models for atherosclerotic plaque in internal carotid artery B-mode ultrasound.
Computers in biology and medicine
Aug 2, 2021
The automated and accurate carotid plaque segmentation in B-mode ultrasound (US) is an essential part of stroke risk stratification. Previous segmented methods used AtheroEdge™ 2.0 (AtheroPoint™, Roseville, CA) for the common carotid artery (CCA). Th...
Deep Learning-Based Carotid Plaque Segmentation from B-Mode Ultrasound Images.
Ultrasound in medicine & biology
Jul 1, 2021
Carotid ultrasound measurement of total plaque area (TPA) provides a method for quantifying carotid plaque burden and monitoring changes in carotid atherosclerosis in response to medical treatment. Plaque boundary segmentation is required to generate...
Advanced deep learning methodology for accurate, real-time segmentation of high-resolution intravascular ultrasound images.
International journal of cardiology
Jun 18, 2021
AIMS: The aim of this study is to develop and validate a deep learning (DL) methodology capable of automated and accurate segmentation of intravascular ultrasound (IVUS) image sequences in real-time.
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