Ultrasound-Based Image Analysis for Predicting Carotid Artery Stenosis Risk: A Comprehensive Review of the Problem, Techniques, Datasets, and Future Directions

Abstract

The carotid artery is a major blood vessel that supplies blood to the brain. Plaque buildup in the arteries can lead to cardiovascular diseases such as atherosclerosis, stroke, ruptured arteries, and even death. Both invasive and non-invasive methods are used to detect plaque buildup in the arteries, with ultrasound imaging being the first line of diagnosis. This paper presents a comprehensive review of the existing literature on ultrasound image analysis methods for detecting and characterizing plaque buildup in the carotid artery. The review includes an in-depth analysis of datasets; image segmentation techniques for the carotid artery plaque area, lumen area, and intima–media thickness (IMT); and plaque measurement, characterization, classification, and stenosis grading using deep learning and machine learning. Additionally, the paper provides an overview of the performance of these methods, including challenges in analysis, and future directions for research.

Hemodynamic Analysis of Carotid Sinus with OpenFOAM and Machine Learning

“…Nowadays, the popularity of using machine learning algorithms has increased in the medical area as in every research area. The studies have been conducted in the detection of carotid artery diseases by machine learning techniques in the literature [12][13][14][15]. Machine learning techniques can be utilized to predict blood flow velocity in the carotid artery, considering various factors such as the geometry of the artery, blood viscosity, arterial walls mechanical properties such as elasticity modulus, Poisson ratio, and density.…”