AIMC Topic: Carotid Arteries
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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...
Predictors of 30-Day Unplanned Readmission After Carotid Artery Stenting Using Artificial Intelligence.
Advances in therapy
Apr 9, 2021
INTRODUCTION: This study aimed to describe the rates and causes of unplanned readmissions within 30 days following carotid artery stenting (CAS) and to use artificial intelligence machine learning analysis for creating a prediction model for short-te...
Automated segmentation of the individual branches of the carotid arteries in contrast-enhanced MR angiography using DeepMedic.
BMC medical imaging
Feb 27, 2021
BACKGROUND: Non-invasive imaging is of interest for tracking the progression of atherosclerosis in the carotid bifurcation, and segmenting this region into its constituent branch arteries is necessary for analyses. The purpose of this study was to va...
Assessing robustness of carotid artery CT angiography radiomics in the identification of culprit lesions in cerebrovascular events.
Scientific reports
Feb 10, 2021
Radiomics, quantitative feature extraction from radiological images, can improve disease diagnosis and prognostication. However, radiomic features are susceptible to image acquisition and segmentation variability. Ideally, only features robust to the...
Ultrasound-based internal carotid artery plaque characterization using deep learning paradigm on a supercomputer: a cardiovascular disease/stroke risk assessment system.
The international journal of cardiovascular imaging
Jan 9, 2021
Visual or manual characterization and classification of atherosclerotic plaque lesions are tedious, error-prone, and time-consuming. The purpose of this study is to develop and design an automated carotid plaque characterization and classification sy...
A Deep Learning Approach to Resolve Aliasing Artifacts in Ultrasound Color Flow Imaging.
IEEE transactions on ultrasonics, ferroelectrics, and frequency control
Nov 24, 2020
Despite being used clinically as a noninvasive flow visualization tool, color flow imaging (CFI) is known to be prone to aliasing artifacts that arise due to fast blood flow beyond the detectable limit. From a visualization standpoint, these aliasing...
Ultrasound Deep Learning for Wall Segmentation and Near-Wall Blood Flow Measurement.
IEEE transactions on ultrasonics, ferroelectrics, and frequency control
May 18, 2020
Studies of medical flow imaging have technical limitations for accurate analysis of blood flow dynamics and vessel wall interaction at arteries. We propose a new deep learning-based boundary detection and compensation (DL-BDC) technique in ultrasound...
Association of Cardiovascular Mortality and Deep Learning-Funduscopic Atherosclerosis Score derived from Retinal Fundus Images.
American journal of ophthalmology
Mar 25, 2020
PURPOSE: The prediction of atherosclerosis using retinal fundus images and deep learning has not been shown possible. The purpose of this study was to develop a deep learning model which predicted atherosclerosis by using retinal fundus images and to...
Deep Learning
Ultrasonography
Follow-Up Studies
Fundus Oculi
Retina
Humans
Predictive Value of Tests
Atherosclerosis
Cardiovascular Diseases
Middle Aged
Female
Risk Assessment
Risk Factors
Adult
Aged
Survival Rate
Prognosis
Aged, 80 and over
Male
Carotid Arteries
Retrospective Studies
Ophthalmoscopy
Republic of Korea
Feasibility of a sub-3-minute imaging strategy for ungated quiescent interval slice-selective MRA of the extracranial carotid arteries using radial k-space sampling and deep learning-based image processing.
Magnetic resonance in medicine
Jan 23, 2020
PURPOSE: To develop and test the feasibility of a sub-3-minute imaging strategy for non-contrast evaluation of the extracranial carotid arteries using ungated quiescent interval slice-selective (QISS) MRA, combining single-shot radial sampling with d...
Bimodal Automated Carotid Ultrasound Segmentation Using Geometrically Constrained Deep Neural Networks.
IEEE journal of biomedical and health informatics
Jan 9, 2020
For asymptomatic patients suffering from carotid stenosis, the assessment of plaque morphology is an important clinical task which allows monitoring of the risk of plaque rupture and future incidents of stroke. Ultrasound Imaging provides a safe and ...
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