AIMC Topic: Plaque, Atherosclerotic

Clear Filters Showing 61 to 70 of 161 articles

Detection of extracranial and intracranial calcified carotid artery atheromas in cone beam computed tomography using a deep learning convolutional neural network image segmentation approach.

Oral surgery, oral medicine, oral pathology and oral radiology
OBJECTIVE: We leveraged an artificial intelligence deep-learning convolutional neural network (DL CNN) to detect calcified carotid artery atheromas (CCAAs) on cone beam computed tomography (CBCT) images.

Accelerated Measurement of Carotid Plaque Volume Using Artificial Intelligence Enhanced 3D Ultrasound.

Annals of vascular surgery
BACKGROUND: Carotid plaque volume (CPV) can be measured by 3D ultrasound and may be a better predictor of stroke than stenosis, but analysis time limits clinical utility. This study tested the accuracy, reproducibility, and time saved of using an art...

Diagnosis of coronary layered plaque by deep learning.

Scientific reports
Healed coronary plaques, morphologically characterized by a layered phenotype, are signs of previous plaque destabilization and healing. Recent optical coherence tomography (OCT) studies demonstrated that layered plaque is associated with higher leve...

[Future of interventional cardiology : Does everything revolve around AI and robotics?].

Herz
In recent years, software-assisted imaging systems, such as computed tomography, have contributed to the improvement of noninvasive options for the diagnostics of coronary heart disease (CHD). In addition, the possibilities of individual morphologica...

Deep learning artificial intelligence framework for multiclass coronary artery disease prediction using combination of conventional risk factors, carotid ultrasound, and intraplaque neovascularization.

Computers in biology and medicine
OBJECTIVE: Cardiovascular disease (CVD) is a major healthcare challenge and therefore early risk assessment is vital. Previous assessment techniques use either "conventional CVD risk calculators (CCVRC)" or machine learning (ML) paradigms. These tech...

Microarchitectural Changes of Cardiovascular Calcification in Response to In Vivo Interventions Using Deep-Learning Segmentation and Computed Tomography Radiomics.

Arteriosclerosis, thrombosis, and vascular biology
BACKGROUND: Coronary calcification associates closely with cardiovascular risk, but its progress is accelerated in response to some interventions widely used to reduce risk. This paradox suggests that qualitative, not just quantitative, changes in ca...

Automatic assessment of calcified plaque and nodule by optical coherence tomography adopting deep learning model.

The international journal of cardiovascular imaging
Optical coherence tomography (OCT) has become the best imaging tool to assess calcified plaque and nodule. However, every OCT pullback has numerous images, and artificial analysis requires too much time and energy. Thus, it is unsuitable for clinical...

Deep learning-based atherosclerotic coronary plaque segmentation on coronary CT angiography.

European radiology
OBJECTIVES: Volumetric evaluation of coronary artery disease (CAD) allows better prediction of cardiac events. However, CAD segmentation is labor intensive. Our objective was to create an open-source deep learning (DL) model to segment coronary plaqu...