AIMC Topic: Plaque, Atherosclerotic

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Automated diagnosis of atherosclerosis using multi-layer ensemble models and bio-inspired optimization in intravascular ultrasound imaging.

Medical & biological engineering & computing Sep 18, 2024
Atherosclerosis causes heart disease by forming plaques in arterial walls. IVUS imaging provides a high-resolution cross-sectional view of coronary arteries and plaque morphology. Healthcare professionals diagnose and quantify atherosclerosis physica...
Coronary Vessels Plaque, Atherosclerotic Image Processing, Computer-Assisted Ultrasonography, Interventional Humans Atherosclerosis Neural Networks, Computer Image Interpretation, Computer-Assisted Algorithms Deep Learning
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Long-term Major Adverse Cardiac Event Prediction by Computed Tomography-derived Plaque Measures and Clinical Parameters Using Machine Learning.

Internal medicine (Tokyo, Japan) Sep 4, 2024
Objective The present study evaluated the usefulness of machine learning (ML) models with the coronary computed tomography imaging and clinical parameters for predicting major adverse cardiac events (MACEs). Methods The Nationwide Gender-specific Ath...
Humans Middle Aged Machine Learning Tomography, X-Ray Computed Prospective Studies Follow-Up Studies Female Predictive Value of Tests Aged Coronary Artery Disease Male Plaque, Atherosclerotic
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Deep learning model for intravascular ultrasound image segmentation with temporal consistency.

The international journal of cardiovascular imaging Aug 27, 2024
This study was conducted to develop and validate a deep learning model for delineating intravascular ultrasound (IVUS) images of coronary arteries.Using a total of 1240 40-MHz IVUS pullbacks with 191,407 frames, the model for lumen and external elast...
Humans Prognosis Retrospective Studies Female Reproducibility of Results Aged Male Ultrasonography, Interventional Middle Aged Coronary Artery Disease Deep Learning Image Interpretation, Computer-Assisted Time Factors Coronary Vessels Predictive Value of Tests Plaque, Atherosclerotic
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An Unsupervised Learning Tool for Plaque Tissue Characterization in Histopathological Images.

Sensors (Basel, Switzerland) Aug 20, 2024
Stroke is the second leading cause of death and a major cause of disability around the world, and the development of atherosclerotic plaques in the carotid arteries is generally considered the leading cause of severe cerebrovascular events. In recent...
Carotid Arteries Plaque, Atherosclerotic Humans Algorithms Image Processing, Computer-Assisted Unsupervised Machine Learning Image Interpretation, Computer-Assisted
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Deep learning improves quality of intracranial vessel wall MRI for better characterization of potentially culprit plaques.

Scientific reports Aug 16, 2024
Intracranial vessel wall imaging (VWI), which requires both high spatial resolution and high signal-to-noise ratio (SNR), is an ideal candidate for deep learning (DL)-based image quality improvement. Conventional VWI (Conv-VWI, voxel size 0.51 × 0.51...
Humans Signal-To-Noise Ratio Plaque, Atherosclerotic Retrospective Studies Female Aged Adult Male Middle Aged Deep Learning Magnetic Resonance Imaging
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Mechanisms of QingRe HuoXue Formula in atherosclerosis Treatment: An integrated approach using Bioinformatics, Machine Learning, and experimental validation.

International immunopharmacology Aug 12, 2024
BACKGROUND: Atherosclerosis (AS) is the main cause of coronary heart disease, cerebral infarction, and peripheral vascular disease. QingRe HuoXue Formula (QRHXF), a common prescription of traditional Chinese medicine, has a definite effect on the cli...
Computational Biology Disease Models, Animal Mice, Inbred C57BL Drugs, Chinese Herbal Atherosclerosis Plaque, Atherosclerotic Collagen Type I Collagen Type I, alpha 1 Chain Animals Male Molecular Docking Simulation Gene Expression Profiling Mice Humans Machine Learning
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Derivation and external validation of mass spectrometry-based proteomic model using machine learning algorithms to predict plaque rupture in patients with acute coronary syndrome.

Clinica chimica acta; international journal of clinical chemistry Aug 6, 2024
BACKGROUND: A poor prognosis is associated with atherosclerotic plaque rupture (PR) despite after conventional therapy for patients with acute coronary syndrome (ACS). Timely identification of PR improves the risk stratification and prognosis of ACS ...
Aged Male Middle Aged Cohort Studies Proteomics Machine Learning Algorithms Biomarkers Female Mass Spectrometry Acute Coronary Syndrome Plaque, Atherosclerotic Humans
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Development of machine learning models for fractional flow reserve prediction in angiographically intermediate coronary lesions.

Catheterization and cardiovascular interventions : official journal of the Society for Cardiac Angiography & Interventions Aug 1, 2024
BACKGROUND: Fractional flow reserve (FFR) represents the gold standard in guiding the decision to proceed or not with coronary revascularization of angiographically intermediate coronary lesion (AICL). Optical coherence tomography (OCT) allows to car...
Software Decision Support Techniques Databases, Factual Female Reproducibility of Results Humans Coronary Vessels Plaque, Atherosclerotic Predictive Value of Tests Cardiac Catheterization Fractional Flow Reserve, Myocardial Tomography, Optical Coherence Coronary Stenosis Aged Male Coronary Angiography Middle Aged Coronary Artery Disease Severity of Illness Index Supervised Machine Learning
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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...
Plaque, Atherosclerotic Computed Tomography Angiography Humans Carotid Stenosis Machine Learning Carotid Artery Diseases Male Middle Aged Severity of Illness Index Predictive Value of Tests Retrospective Studies Carotid Arteries Female Reproducibility of Results Aged
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Artificial Intelligence-Enabled Quantitative Coronary Plaque and Hemodynamic Analysis for Predicting Acute Coronary Syndrome.

JACC. Cardiovascular imaging May 15, 2024
BACKGROUND: A lesion-level risk prediction for acute coronary syndrome (ACS) needs better characterization.
Middle Aged Plaque, Atherosclerotic Risk Assessment Computed Tomography Angiography Severity of Illness Index Acute Coronary Syndrome Coronary Circulation Reproducibility of Results Hemodynamics Time Factors Coronary Vessels Risk Factors Humans Rupture, Spontaneous Predictive Value of Tests Aged Artificial Intelligence Multidetector Computed Tomography Coronary Angiography Male Prognosis Radiographic Image Interpretation, Computer-Assisted Female Coronary Artery Disease
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