Ultrasound-Based Image Analysis for Predicting Carotid Artery Stenosis Risk: A Comprehensive Review of the Problem,...

Area of Science:

• Medical Imaging
• Cardiovascular Diagnostics
• Artificial Intelligence in Medicine

Background:

• Carotid artery plaque buildup is a significant risk factor for cardiovascular diseases, including stroke.
• Ultrasound imaging is the primary non-invasive diagnostic tool for assessing carotid artery plaque.
• Accurate detection and characterization of plaque are crucial for patient management and risk stratification.

Purpose of the Study:

• To comprehensively review existing literature on ultrasound image analysis methods for carotid artery plaque detection and characterization.
• To analyze datasets, segmentation techniques, and machine learning/deep learning approaches for plaque analysis.
• To identify current challenges and future research directions in the field.

Main Methods:

• Literature review of ultrasound image analysis techniques for carotid artery plaque.
• Analysis of image segmentation methods for plaque area, lumen area, and intima-media thickness (IMT).
• Examination of deep learning and machine learning applications for plaque measurement, characterization, classification, and stenosis grading.

Main Results:

• The review synthesizes various methods for analyzing carotid artery ultrasound images.
• It highlights the application of AI, including deep learning and machine learning, in plaque analysis.
• Performance metrics, challenges, and limitations of current methods are discussed.

Conclusions:

• Advanced image analysis techniques, particularly AI-driven methods, show promise in improving the detection and characterization of carotid artery plaque.
• Further research is needed to address current challenges and enhance the clinical utility of these methods.
• Accurate plaque assessment is vital for preventing cardiovascular events like stroke.