Ultrasound-Based Image Analysis for Predicting Carotid Artery Stenosis Risk: A Comprehensive Review of the Problem, Techniques, Datasets, and Future Directions.
US
carotid artery stenosis risk
classification
computer vision
deep learning
machine learning
plaque characterization
segmentation
Journal
Diagnostics (Basel, Switzerland)
ISSN: 2075-4418
Titre abrégé: Diagnostics (Basel)
Pays: Switzerland
ID NLM: 101658402
Informations de publication
Date de publication:
07 Aug 2023
07 Aug 2023
Historique:
received:
15
06
2023
revised:
25
07
2023
accepted:
25
07
2023
medline:
12
8
2023
pubmed:
12
8
2023
entrez:
12
8
2023
Statut:
epublish
Résumé
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.
Identifiants
pubmed: 37568976
pii: diagnostics13152614
doi: 10.3390/diagnostics13152614
pmc: PMC10417708
pii:
doi:
Types de publication
Journal Article
Review
Langues
eng
Subventions
Organisme : Qatar University
ID : QUHI-CENG-22/23-548
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