Atherosclerotic plaque detection by carotid ultrasound provides cardiovascular disease risk stratification. The advantages and disadvantages of two-dimensional (2D) and three-dimensional (3D) ultrasound methods for carotid arterial plaque quantification are reviewed.

2016年3月5日 · The International Classification System describes the plaque as either uniformly or predominately sonolucent (hypoechoic) or echogenic (hyperechoic). The Bluth classification utilizes heterogeneous as greater than 50% sonolucent (hypoechoic) and homogeneous to describe plaque that is greater than 50% echogenic (hyperechoic).

The carotid plaques described as anechoic or “echolucent” or “predominantly echolucent”—those that correspond to types 1 and 2 in the G-W classification—are unstable plaques that can become symptomatic, regardless of whether or not they are associated with stenosis.

2023年3月1日 · Echo classification of plaques in ultrasound videos is provided. This is the first study to deeply combine plaque tracking and classification. This framework is robust in identifying plaques with inconsistent echo in video. The risk assessment of carotid plaque is strongly related to the plaque echo status in ultrasound.

2024年5月4日 · Ultrasound assessment of carotid arterial atherosclerotic disease has become the first choice for carotid artery stenosis screening, permitting the evaluation of both the macroscopic appearance of plaques and flow characteristics in the carotid artery.

2024年2月9日 · The recommendations for the assessment of carotid arterial plaque by ultrasound for the characterisation of Atherosclerosis and Evaluation of Cardiovascular Risk, recently published by the American Society of Echocardiography, focuses on the ultrasound methods used to quantify carotid plaque, when present, for the purpose of cardiovascular risk ...

The classification incorporates carotid ultrasound, carotid computed tomography (CT) angiography, magnetic resonance imaging (MRI), and positron emission tomography (PET)-CT, to be used when certain plaque characteristics that make it a high risk are suspected, so that patients’ risk can be better delineated.

2024年5月1日 · In the present study, we applied a machine learning (ML)-based method to classify atherosclerotic plaques in B-mode ultrasound images of carotid arteries. A series of 23 grayscale features was extracted and classified using a support vector machine (SVM) algorithm with a linear kernel function.

1999年1月1日 · Background and Purpose —Clinical studies currently in progress are using subjective methods to characterize plaque morphology from ultrasound images. However, there are few studies on the intraobserver and interobserver variability of these classifications. This study was designed to assess these variables.

2021年8月27日 · 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). Methods and analysis: A prospective multicentre study was conducted to assess vulnerability in patients with carotid plaque. Data from 547 ...