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Related Concept Videos

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

Imaging Studies for Cardiovascular System VI: Calcium -Scoring CT

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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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Imaging Studies for Cardiovascular System V: CT01:28

Imaging Studies for Cardiovascular System V: CT

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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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Imaging Studies for Cardiovascular System III: X-Ray01:20

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The most common cardiovascular diagnostic test is an X-ray. It produces images of the heart, blood vessels, and adjacent structures.
Definition and Purpose
An X-ray, or radiograph, is a non-invasive method that uses ionizing radiation to take images of internal structures. It is mainly used in cardiac imaging to examine the heart, lungs, and major blood vessels, aiming to identify abnormalities in the heart's size, shape, and position, such as heart failure, congenital defects, and vascular...
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Updated: Oct 13, 2025

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Deep-Learning-Based Coronary Artery Calcium Detection from CT Image.

Sungjin Lee1, Beanbonyka Rim1, Sung-Shick Jou2

  • 1Department of Software Convergence, Soonchunhyang University, Asan 31538, Korea.

Sensors (Basel, Switzerland)
|November 13, 2021
PubMed
Summary
This summary is machine-generated.

This study demonstrates an automated method for detecting coronary artery calcium using deep learning. Resnet 50 achieved 98.52% accuracy on segmented cardiac images, improving coronary artery disease diagnosis efficiency.

Keywords:
VGGcalcium detectioncoronary artery calcium score CTdeep learningimage classificationinception resnet V2resnet-50

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Area of Science:

  • Cardiovascular Imaging
  • Artificial Intelligence in Medicine
  • Radiology

Background:

  • Coronary artery disease diagnosis relies on coronary artery calcium score CT, a time-consuming manual process.
  • Current methods require radiologists to manually inspect numerous CT images, increasing diagnostic time.

Purpose of the Study:

  • To develop and evaluate deep learning models for automated coronary artery calcium detection.
  • To improve the efficiency and accuracy of coronary artery calcium scoring in cardiovascular CT images.

Main Methods:

  • Three Convolutional Neural Network (CNN) models (Inception Resnet v2, VGG, Resnet 50) were applied to 2400 cardiovascular CT images.
  • CT image data was classified into original, segmented cardiac, and cropped cardiac images for analysis.

Main Results:

  • The Resnet 50 model achieved the highest accuracy of 98.52% when applied to cardiac cropped image data.
  • This demonstrates the effectiveness of deep learning in identifying coronary artery calcium.

Conclusions:

  • Automated detection of coronary artery calcium using deep learning is feasible and highly accurate.
  • Further research may enable full automation of calcium presence detection and scoring for coronary artery calcium CT.