AIMC Topic: Vascular Calcification

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Evaluation of a Deep Learning-Based Automated CT Coronary Artery Calcium Scoring Algorithm.

JACC. Cardiovascular imaging Nov 13, 2019
Software Retrospective Studies Female Reproducibility of Results Aged Humans Electrocardiography Predictive Value of Tests Coronary Angiography Radiographic Image Interpretation, Computer-Assisted Coronary Artery Disease Male Middle Aged Severity of Illness Index Diagnosis, Computer-Assisted Deep Learning Cardiac-Gated Imaging Techniques Automation Coronary Vessels Vascular Calcification Computed Tomography Angiography
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Coronary artery calcium score quantification using a deep-learning algorithm.

Clinical radiology Nov 11, 2019
AIM: To investigate the impact of a deep-learning algorithm on the quantification of coronary artery calcium score (CACS) and the stratification of cardiac risk.
Aged Male Middle Aged Deep Learning Electrocardiography Feasibility Studies Coronary Disease Vascular Calcification Humans Retrospective Studies Tomography, X-Ray Computed Female
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Motion-corrected coronary calcium scores by a convolutional neural network: a robotic simulating study.

European radiology Oct 18, 2019
OBJECTIVE: To classify motion-induced blurred images of calcified coronary plaques so as to correct coronary calcium scores on nontriggered chest CT, using a deep convolutional neural network (CNN) trained by images of motion artifacts.
Artifacts Coronary Vessels Plaque, Atherosclerotic Vascular Calcification Humans Neural Networks, Computer Algorithms Tomography, X-Ray Computed Robotics Coronary Artery Disease Motion
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Machine learning based risk prediction model for asymptomatic individuals who underwent coronary artery calcium score: Comparison with traditional risk prediction approaches.

Journal of cardiovascular computed tomography Sep 23, 2019
BACKGROUND: Machine learning (ML) is a computer algorithm used to identify patterns for prediction in various tasks, and ML methods have been beneficial for developing prediction models when applied to heterogeneous and large datasets. We aim to exam...
Coronary Artery Disease Vascular Calcification Asymptomatic Diseases Time Factors Health Status Predictive Value of Tests Coronary Angiography Registries Adult Male Middle Aged Risk Assessment Humans Machine Learning Decision Support Techniques Databases, Factual Prognosis Retrospective Studies Republic of Korea Female Risk Factors
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Influence of Coronary Calcium on Diagnostic Performance of Machine Learning CT-FFR: Results From MACHINE Registry.

JACC. Cardiovascular imaging Aug 14, 2019
OBJECTIVES: This study was conducted to investigate the influence of coronary artery calcium (CAC) score on the diagnostic performance of machine-learning-based coronary computed tomography (CT) angiography (cCTA)-derived fractional flow reserve (CT-...
North America Humans Aged Machine Learning Male Coronary Artery Disease Female Middle Aged Reproducibility of Results Vascular Calcification Severity of Illness Index Asia Diagnosis, Computer-Assisted Europe Computed Tomography Angiography Predictive Value of Tests Fractional Flow Reserve, Myocardial Coronary Angiography Radiographic Image Interpretation, Computer-Assisted Registries
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Fibroblast growth factor23 is associated with axonal integrity and neural network architecture in the human frontal lobes.

PloS one Sep 7, 2018
Elevated levels of FGF23 in individuals with chronic kidney disease (CKD) are associated with adverse health outcomes, such as increased mortality, large vessel disease, and reduced white matter volume, cardiovascular and cerebrovascular events. Apar...
Magnetic Resonance Imaging Vascular Stiffness Fibroblast Growth Factor-23 Fibroblast Growth Factors Axons Male Endothelium, Vascular Cardiovascular Diseases White Matter Middle Aged Vascular Calcification Cohort Studies Frontal Lobe Renal Insufficiency, Chronic Humans Neural Networks, Computer Female Risk Factors
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Machine learning in cardiac CT: Basic concepts and contemporary data.

Journal of cardiovascular computed tomography Apr 30, 2018
Propelled by the synergy of the groundbreaking advancements in the ability to analyze high-dimensional datasets and the increasing availability of imaging and clinical data, machine learning (ML) is poised to transform the practice of cardiovascular ...
Computed Tomography Angiography Coronary Angiography Radiographic Image Interpretation, Computer-Assisted Coronary Artery Disease Coronary Vessels Vascular Calcification Prognosis Reproducibility of Results Severity of Illness Index Predictive Value of Tests Humans Machine Learning Algorithms
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Integrated prediction of lesion-specific ischaemia from quantitative coronary CT angiography using machine learning: a multicentre study.

European radiology Jan 19, 2018
OBJECTIVES: We aimed to investigate if lesion-specific ischaemia by invasive fractional flow reserve (FFR) can be predicted by an integrated machine learning (ML) ischaemia risk score from quantitative plaque measures from coronary computed tomograph...
Male Middle Aged Severity of Illness Index Humans Hemodynamics Coronary Angiography Coronary Stenosis Machine Learning Fractional Flow Reserve, Myocardial Female Coronary Artery Disease Aged Myocardial Ischemia Aged, 80 and over Plaque, Atherosclerotic Adult Vascular Calcification Computed Tomography Angiography
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Identification of potential biomarkers in cardiovascular calcification based on bioinformatics combined with single-cell RNA-seq and multiple machine learning analysis.

Cellular signalling Jul 1, 2025
BACKGROUND: The molecular and genetic mechanisms underlying vascular calcification remain unclear. This study aimed to determine the differences in calcification marker-related gene expression in macrophages.
Single-Cell Gene Expression Analysis Mice Computational Biology Vascular Calcification Biomarkers Animals Single-Cell Analysis Protein Interaction Maps Humans RNA-Seq Machine Learning
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A multi-model deep learning approach for the identification of coronary artery calcifications within 2D coronary angiography images.

International journal of computer assisted radiology and surgery Jun 1, 2025
PURPOSE: Identifying and quantifying coronary artery calcification (CAC) is crucial for preoperative planning, as it helps to estimate both the complexity of the 2D coronary angiography (2DCA) procedure and the risk of developing intraoperative compl...
Humans Radiographic Image Interpretation, Computer-Assisted Coronary Artery Disease Workflow Coronary Vessels Vascular Calcification Female Male Middle Aged Deep Learning Coronary Angiography
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