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Imaging Studies for Cardiovascular System VI: Calcium -Scoring CT01:25

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Calcium-Scoring CT ScanA calcium-scoring CT scan, also known as coronary artery calcium (CAC) scan, detects calcium deposits in the coronary arteries. This test assesses the risk of coronary artery disease (CAD), which can lead to cardiovascular events such as angina, heart failure, and sudden cardiac arrest.A calcium-scoring CT scan is generally recommended for individuals at intermediate risk of CAD without symptoms. It includes:Men aged 40-75 and women aged 50-75: Especially those with a...
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Cardiac computed tomography (CT) scanning is an advanced cardiac imaging technique that utilizes CT technology, with or without intravenous (IV) contrast, to produce accurate cross-sectional virtual slices of specific areas of the heart, coronary circulation, and major blood vessels such as the aorta, pulmonary veins, and arteries. The computer processes these slices to generate three-dimensional images. Multidetector CT (MDCT) is a rapid form of CT scanning that captures multiple slices...
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Tomography refers to imaging by sections. Computed tomography (CT) is a non-invasive imaging technique that uses computers to analyze several cross-sectional X-rays to reveal minute details about structures in the body.
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Cardiovascular magnetic resonance imaging, or CMRI, is a non-invasive diagnostic test that employs a magnetic field and radiofrequency waves to create precise images of the heart and arteries. It provides comprehensive information about cardiac anatomy, function, perfusion, and tissue characterization without ionizing radiation.IndicationsCMRI diagnoses various heart conditions, including tissue damage from heart attacks, ischemic heart disease, myocarditis, aortic issues (tears, aneurysms,...
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DefinitionComputed Tomography (CT) of the genitourinary (GU) tract is a non-invasive imaging modality that utilizes X-rays and computer processing to generate detailed cross-sectional images of the urinary system, encompassing the kidneys, ureters, bladder, and adjacent structures such as the adrenal glands.PurposeCT scans of the GU tract serve several diagnostic and therapeutic purposes, including:Diagnosis of Urinary Tract Diseases: Detects kidney stones, tumors, cysts, and congenital...
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Automated Coronary Artery Calcium Scoring Using Deep Learning: Validation Across Diverse Chest CT Protocols.

Eduardo Mineo1, Antonildes N Assuncao-Jr1, Carla Franco Grego da Silva1

  • 1Heart Institute (InCor), Hospital das Clínicas HCFMUSP, Faculdade de Medicina, Universidade de São Paulo, São Paulo, São Paulo, Brazil.

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|September 25, 2025
PubMed
Summary
This summary is machine-generated.

A new deep learning model accurately quantifies coronary artery calcium (CAC) on routine chest CT scans, enabling efficient cardiovascular risk assessment. This automated tool supports opportunistic screening for atherosclerotic cardiovascular disease (ASCVD).

Keywords:
Cardiovascular riskChest computed tomographyCoronary artery calciumDeep learningOpportunistic screening

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Area of Science:

  • Radiology and Medical Imaging
  • Artificial Intelligence in Healthcare
  • Cardiovascular Disease Risk Assessment

Background:

  • Coronary artery calcium (CAC) scoring is crucial for atherosclerotic cardiovascular disease (ASCVD) risk stratification.
  • Routine non-gated chest CT (NCCT) use has increased, presenting an opportunity for opportunistic CAC assessment.
  • Current methods for CAC quantification on NCCT are not widely integrated into clinical workflows.

Purpose of the Study:

  • To develop and validate a deep learning (DL) model for automated, protocol-agnostic CAC quantification.
  • To enable workflow-ready CAC scoring from routine chest CT scans.
  • To support opportunistic cardiovascular risk stratification in clinical practice.

Main Methods:

  • A retrospective study involving 2132 chest CT scans (routine, CT-CAC, CT-COVID) from patients without established ASCVD.
  • Training and validation of a DL-based CAC segmentation model against manual annotations.
  • Evaluation of agreement using intra-class correlation coefficients (ICC) and Cohen's kappa.
  • Calculation of diagnostic performance metrics including sensitivity, specificity, and F1 scores.

Main Results:

  • The DL model showed high reliability for Agatston scores (ICC=0.987) and strong agreement in CAC categories (Cohen's κ=0.86-0.95).
  • Excellent diagnostic performance was observed for CAC >100 (F1=0.956) and CAC >300 (F1=0.967).
  • Good agreement was confirmed through external validation in the Mashhad COVID Study (κ=0.8) and SBU COVID study (F1=0.928 for moderate-to-severe CAC).

Conclusions:

  • The developed DL model provides accurate and workflow-ready CAC quantification.
  • The model is effective across various chest CT scan types, including routine and pandemic-era scans.
  • This technology supports cost-effective, opportunistic cardiovascular risk stratification in clinical settings.