AIMC Topic: Vascular Calcification

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Artificial intelligence-derived coronary artery calcium scoring saves time and achieves close to radiologist-level accuracy accuracy on routine ECG-gated CT.

The international journal of cardiovascular imaging Dec 16, 2024
Artificial Intelligence (AI) has been proposed to improve workflow for coronary artery calcium scoring (CACS), but simultaneous demonstration of improved efficiency, accuracy, and clinical stability have not been demonstrated. 148 sequential patients...
Artificial Intelligence Retrospective Studies Female Reproducibility of Results Aged Humans Predictive Value of Tests Coronary Angiography Radiologists Radiographic Image Interpretation, Computer-Assisted Coronary Artery Disease Male Middle Aged Severity of Illness Index Time Factors Electrocardiography Vascular Calcification Computed Tomography Angiography Cardiac-Gated Imaging Techniques Workflow Observer Variation Coronary Vessels Multidetector Computed Tomography
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Quantification of coronary artery calcification in systemic sclerosis using visual ordinal and deep learning scoring: Association with systemic sclerosis clinical features.

Seminars in arthritis and rheumatism Nov 20, 2024
OBJECTIVE: To investigate the association between systemic sclerosis (SSc) clinical features and the extent and progression of coronary artery calcifications.
Humans Retrospective Studies Female Severity of Illness Index Adult Aged Deep Learning Male Coronary Artery Disease Middle Aged Vascular Calcification Disease Progression Scleroderma, Systemic
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Impact of tooth loss and patient characteristics on coronary artery calcium score classification and prediction.

Scientific reports Nov 16, 2024
This study, for the first time, explores the integration of data science and machine learning for the classification and prediction of coronary artery calcium (CAC) scores. It focuses on tooth loss and patient characteristics as key input features to...
Coronary Vessels Vascular Calcification Tooth Loss Humans Machine Learning Adult Female Male Risk Factors Middle Aged Aged Coronary Artery Disease Calcium
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Evaluation of a deep learning-based software to automatically detect and quantify breast arterial calcifications on digital mammogram.

Diagnostic and interventional imaging Oct 26, 2024
PURPOSE: The purpose of this study was to evaluate an artificial intelligence (AI) software that automatically detects and quantifies breast arterial calcifications (BAC).
Deep Learning Mammography Radiographic Image Interpretation, Computer-Assisted Breast Vascular Calcification Breast Diseases Aged Aged, 80 and over Adult Sensitivity and Specificity Middle Aged Software Retrospective Studies Tomography, X-Ray Computed Female Humans
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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...
Female Aged Male Middle Aged Radiography, Panoramic Humans Deep Learning Neural Networks, Computer Carotid Arteries Plaque, Atherosclerotic Vascular Calcification Carotid Artery Diseases
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Meta-analysis of deep learning approaches for automated coronary artery calcium scoring: Performance and clinical utility AI in CAC scoring: A meta-analysis: AI in CAC scoring: A meta-analysis.

Computers in biology and medicine Oct 23, 2024
INTRODUCTION: Manual Coronary Artery Calcium (CAC) scoring, crucial for assessing coronary artery disease risk, is time-consuming and variable. Deep learning, particularly through Convolutional Neural Networks (CNNs), promises to automate and enhance...
Calcium Coronary Vessels Vascular Calcification Humans Artificial Intelligence Deep Learning Coronary Artery Disease
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Automated AI-based coronary calcium scoring using retrospective CT data from SCAPIS is accurate and correlates with expert scoring.

European radiology Oct 18, 2024
OBJECTIVES: Evaluation of the correlation and agreement between AI and semi-automatic evaluations of calcium scoring CT (CSCT) examinations using extensive data from the Swedish CardioPulmonary bio-Image study (SCAPIS).
Humans Artificial Intelligence Male Middle Aged Radiographic Image Interpretation, Computer-Assisted Coronary Artery Disease Vascular Calcification Retrospective Studies Tomography, X-Ray Computed Female Reproducibility of Results Sweden
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Energy-integrating detector based ultra-high-resolution CT with deep learning reconstruction for the assessment of calcified lesions in coronary artery disease.

Journal of cardiovascular computed tomography Oct 8, 2024
BACKGROUND: The aim of this study to compare of the image quality of calcified lesions in coronary artery disease between deep learning reconstruction (DLR) and model-based iterative reconstruction (MBIR) on energy-integrating detector (EID) based ul...
Vascular Calcification Phantoms, Imaging Computed Tomography Angiography Middle Aged Severity of Illness Index Deep Learning Predictive Value of Tests Coronary Angiography Radiographic Image Interpretation, Computer-Assisted Coronary Artery Disease Coronary Vessels Multidetector Computed Tomography Retrospective Studies Female Reproducibility of Results Aged Male Humans
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Ultralow dose coronary calcium scoring CT at reduced tube voltage and current by using deep learning image reconstruction.

European journal of radiology Sep 19, 2024
OBJECTIVE: To explore the potential of the deep learning reconstruction (DLR) for ultralow dose calcium scoring CT (CSCT) with simultaneously reduced tube voltage and current.
Radiographic Image Interpretation, Computer-Assisted Aged Coronary Artery Disease Humans Prospective Studies Tomography, X-Ray Computed Radiation Dosage Female Male Vascular Calcification Middle Aged Deep Learning Coronary Angiography
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Fully automated epicardial adipose tissue volume quantification with deep learning and relationship with CAC score and micro/macrovascular complications in people living with type 2 diabetes: the multicenter EPIDIAB study.

Cardiovascular diabetology Sep 3, 2024
BACKGROUND: The aim of this study (EPIDIAB) was to assess the relationship between epicardial adipose tissue (EAT) and the micro and macrovascular complications (MVC) of type 2 diabetes (T2D).
Deep Learning Diabetes Mellitus, Type 2 Adipose Tissue Vascular Calcification Radiographic Image Interpretation, Computer-Assisted Predictive Value of Tests Humans Computed Tomography Angiography Aged Coronary Artery Disease Coronary Angiography Prognosis Pericardium Adiposity Male Female Epicardial Adipose Tissue Middle Aged Automation Reproducibility of Results Risk Assessment Diabetic Angiopathies Risk Factors
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