AIMC Topic: Vascular Calcification

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Interpretable and reproducible machine learning model for coronary calcification and segment-level stenoses stratification on computed tomography angiography.

BMC medicine
BACKGROUND: Coronary computed tomography angiography (CCTA) is widely used as a first-line tool for diagnosing and managing coronary artery disease (CAD), and machine learning (ML)-based analysis shows promise for quantitative CAD assessment.

AI-based modality-agnostic classification system for vascular calcifications.

Scientific reports
The importance of vascular calcification in major adverse cardiovascular events such as heart attacks or strokes has been established. However, calcifications have heterogeneous phenotypes, and their influence on diseased tissue stability remains poo...

Pulse wave-driven machine learning for the non-invasive assessment of coronary artery calcification in patients with end-stage renal disease undergoing hemodialysis.

Biomedical engineering online
BACKGROUND: Coronary artery calcification (CAC) represents a major cardiovascular risk in patients with end-stage renal disease (ESRD) undergoing hemodialysis. Given that radial artery pulse waveforms can reflect vascular status, this study aimed to ...

Segmentation of coronary calcifications with a domain knowledge-based lightweight 3D convolutional neural network.

Computers in biology and medicine
Cardiovascular diseases are the leading cause of death in the world, with coronary artery disease being the most prevalent. Coronary artery calcifications are critical biomarkers for cardiovascular disease, and their quantification via non-contrast c...

Incidental Finding of Coronary and Non-Coronary Artery Calcium: What Do Clinicians Need To Know?

Current atherosclerosis reports
PURPOSE OF REVIEW: This review summarizes the role of incidentally and non-incidentally discovered coronary artery calcification (CAC) and the evolving role of non-coronary artery calcification in atherosclerotic cardiovascular disease (ASCVD) risk a...

Prediction of Percutaneous Coronary Intervention Success in Patients With Moderate to Severe Coronary Artery Calcification Using Machine Learning Based on Coronary Angiography: Prospective Cohort Study.

Journal of medical Internet research
BACKGROUND: Given the challenges faced during percutaneous coronary intervention (PCI) for heavily calcified lesions, accurately predicting PCI success is crucial for enhancing patient outcomes and optimizing procedural strategies.

Artificial intelligence-assisted longitudinal assessment of coronary artery calcification in the Korean lung cancer screening CT program.

Clinical imaging
PURPOSE: The clinical implications of coronary artery calcification (CAC) growth remain underexplored. This study aims to assess CAC growth and its association with adverse cardiovascular events (ACEs) in individuals undergoing lung cancer screening ...

Quantification of Breast Arterial Calcification in Mammograms Using a UNet-Based Deep Learning for Detecting Cardiovascular Disease.

Academic radiology
BACKGROUND: Breast arterial calcification (BAC) is increasingly recognized as a significant indicator of cardiovascular risk, necessitating improvements in detection and quantification methods through mammographic screening.