AIMC Topic: Plaque, Atherosclerotic
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Automated Coronary Artery Segmentation with 3D PSPNET using Global Processing and Patch Based Methods on CCTA Images.
Cardiovascular engineering and technology
Feb 20, 2025
The prevalence of coronary artery disease (CAD) has become the major cause of death across the world in recent years. The accurate segmentation of coronary artery is important in clinical diagnosis and treatment of coronary artery disease (CAD) such ...
PlaqueViT: a vision transformer model for fully automatic vessel and plaque segmentation in coronary computed tomography angiography.
European radiology
Feb 5, 2025
OBJECTIVES: To develop and evaluate a deep learning model for segmentation of the coronary artery vessels and coronary plaques in coronary computed tomography angiography (CCTA).
Ultra-low-dose coronary CT angiography via super-resolution deep learning reconstruction: impact on image quality, coronary plaque, and stenosis analysis.
European radiology
Feb 1, 2025
OBJECTIVES: To exploit the capability of super-resolution deep learning reconstruction (SR-DLR) to save radiation exposure from coronary CT angiography (CCTA) and assess its impact on image quality, coronary plaque quantification and characterization...
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...
Radiomics and deep learning features of pericoronary adipose tissue on non-contrast computerized tomography for predicting non-calcified plaques.
Journal of X-ray science and technology
Dec 18, 2024
BACKGROUND: Inflammation of coronary arterial plaque is considered a key factor in the development of coronary heart disease. Early the plaque detection and timely treatment of the atherosclerosis could effectively reduce the risk of cardiovascular e...
Machine learning-based radiomic features of perivascular adipose tissue in coronary computed tomography angiography predicting inflammation status around atherosclerotic plaque: a retrospective cohort study.
Annals of medicine
Dec 12, 2024
OBJECTIVES: This study expolored the relationship between perivascular adipose tissue (PVAT) radiomic features derived from coronary computed tomography angiography (CCTA) and the presence of coronary artery plaques. It aimed to determine whether PVA...
Automated Classification of Coronary Plaque on Intravascular Ultrasound by Deep Classifier Cascades.
IEEE transactions on ultrasonics, ferroelectrics, and frequency control
Nov 27, 2024
Intravascular ultrasound (IVUS) is the gold standard modality for in vivo visualization of coronary arteries and atherosclerotic plaques. Classification of coronary plaques helps to characterize heterogeneous components and evaluate the risk of plaqu...
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...
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