AIMC Topic: Carotid Artery Diseases
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Research on ischemic stroke risk assessment based on CTA radiomics and machine learning.
BMC medical imaging
Jun 5, 2025
BACKGROUND: The study explores the value of a model constructed by integrating CTA-based carotid plaque radiomic features, clinical risk factors, and plaque imaging characteristics for prognosticating the risk of ischemic stroke.
Exploring the role of oxidative stress in carotid atherosclerosis: insights from transcriptomic data and single-cell sequencing combined with machine learning.
Biology direct
Jan 29, 2025
BACKGROUND: Carotid atherosclerotic plaque is the primary cause of cardiovascular and cerebrovascular diseases. It is closely related to oxidative stress and immune inflammation. This bioinformatic study was conducted to identify key oxidative stress...
Advances in the Application of Artificial Intelligence in the Ultrasound Diagnosis of Vulnerable Carotid Atherosclerotic Plaque.
Ultrasound in medicine & biology
Jan 18, 2025
Vulnerable atherosclerotic plaque is a type of plaque that poses a significant risk of high mortality in patients with cardiovascular disease. Ultrasound has long been used for carotid atherosclerosis screening and plaque assessment due to its safety...
Varying pixel resolution significantly improves deep learning-based carotid plaque histology segmentation.
Scientific reports
Jan 2, 2025
Carotid plaques-the buildup of cholesterol, calcium, cellular debris, and fibrous tissues in carotid arteries-can rupture, release microemboli into the cerebral vasculature and cause strokes. The likelihood of a plaque rupturing is thought to be asso...
Detection of carotid plaques on panoramic radiographs using deep learning.
Journal of dentistry
Oct 24, 2024
OBJECTIVES: Panoramic radiographs (PRs) can reveal an incidental finding of atherosclerosis, or carotid artery calcification (CAC), in 3-15% of examined patients. However, limited training in identification of such calcifications among dental profess...
Assessing the impact of ultrasound image standardization in deep learning-based segmentation of carotid plaque types.
Computer methods and programs in biomedicine
Oct 10, 2024
BACKGROUND AND OBJECTIVE: Carotid B-mode ultrasound (CBUS) imaging is often used to detect and assess atherosclerotic plaques. Doctors often need to segment plaques in the CBUS images to further examine them. Multiple studies have proposed two-dimens...
A stacking ensemble model for predicting the occurrence of carotid atherosclerosis.
Frontiers in endocrinology
Jul 23, 2024
BACKGROUND: Carotid atherosclerosis (CAS) is a significant risk factor for cardio-cerebrovascular events. The objective of this study is to employ stacking ensemble machine learning techniques to enhance the prediction of CAS occurrence, incorporatin...
Machine Learning Detects Symptomatic Plaques in Patients With Carotid Atherosclerosis on CT Angiography.
Circulation. Cardiovascular imaging
Jun 18, 2024
BACKGROUND: This study aimed to develop and validate a computed tomography angiography based machine learning model that uses plaque composition data and degree of carotid stenosis to detect symptomatic carotid plaques in patients with carotid athero...
CACSNet for automatic robust classification and segmentation of carotid artery calcification on panoramic radiographs using a cascaded deep learning network.
Scientific reports
Jun 17, 2024
Stroke is one of the major causes of death worldwide, and is closely associated with atherosclerosis of the carotid artery. Panoramic radiographs (PRs) are routinely used in dental practice, and can be used to visualize carotid artery calcification (...
Deep learning-based fully automatic screening of carotid artery plaques in computed tomography angiography: a multicenter study.
Clinical radiology
May 6, 2024
AIM: To develop and validate a deep learning (DL) algorithm for the automated detection and classification of carotid artery plaques (CAPs) on computed tomography angiography (CTA) images.
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