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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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Identifying Coronary Artery Calcification on Non-gated Computed Tomography Scans
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A Novel Fully Automated Deep Learning Model for Coronary Artery Calcification Detection on Computed Tomography.

Turki Nasser Alnasser1,2,3, Alireza Hokmabadi1,4, Michael J Sharkey1,5

  • 1School of Medicine & Population Health, The University of Sheffield, Sheffield S10 2TN, UK.

Diagnostics (Basel, Switzerland)
|March 14, 2026
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A deep learning (DL) model accurately segments coronary arteries and detects calcifications on CT scans. This automated tool shows high diagnostic accuracy, aiding in predicting coronary artery disease severity.

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calcificationscoronarynon-contrast CTpulmonary hypertension

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Area of Science:

  • Cardiology
  • Medical Imaging
  • Artificial Intelligence

Background:

  • Coronary artery disease (CAD) is a leading cause of mortality.
  • Accurate detection and quantification of coronary artery calcifications (CACs) are crucial for risk stratification.
  • Current methods for CAC analysis can be time-consuming and subject to inter-observer variability.

Purpose of the Study:

  • To evaluate a fully automated deep learning (DL) model for coronary artery segmentation and calcification detection.
  • To assess the diagnostic accuracy of the DL model on non-contrast, non-gated CT scans.
  • To determine the model's ability to predict coronary artery disease severity based on calcification volume.

Main Methods:

  • A two-stage 3D segmentation pipeline was developed for coronary artery identification and calcification detection.
  • The DL model was trained using anatomically refined labels and region-based optimization.
  • Performance was evaluated against manual annotations in a large cohort (473 scans) and visually assessed by expert radiologists.

Main Results:

  • The DL model demonstrated excellent performance in visual assessments and strong agreement with manual reference standards.
  • High accuracy was achieved for coronary artery segmentation (κ 0.68–0.81) and calcification detection (κ 0.79–0.85).
  • The model achieved high diagnostic accuracy for calcification detection (sensitivity 95%, specificity 98%) and correlated well with radiologist-reported disease severity.

Conclusions:

  • The developed DL model accurately segments coronary arteries and detects calcifications on non-contrast CT.
  • The model shows high diagnostic accuracy and effectively predicts coronary artery disease severity.
  • This automated approach holds promise for efficient and reliable CAD assessment.