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Updated: Oct 13, 2025
Author Spotlight: Advancing Cardiovascular Imaging - Introducing the Spatially Weighted Calcium Score for Early Disease Detection
Published on: September 22, 2023
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.
Insights
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.
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.
Abstract:
One of the most common methods for diagnosing coronary artery disease is the use of the coronary artery calcium score CT. However, the current diagnostic method using the coronary artery calcium score CT requires a considerable time, because the radiologist must manually check the CT images one-by-one, and check the exact range. In this paper, three CNN models are applied for 1200 normal cardiovascular CT images, and 1200 CT images in which calcium is present in the cardiovascular system. We conduct the experimental test by classifying the CT image data into the original coronary artery calcium score CT images containing the entire rib cage, the cardiac segmented images that cut out only the heart region, and cardiac cropped images that are created by using the cardiac images that are segmented into nine sub-parts and enlarged. As a result of the experimental test to determine the presence of calcium in a given CT image using Inception Resnet v2, VGG, and Resnet 50 models, the highest accuracy of 98.52% was obtained when cardiac cropped image data was applied using the Resnet 50 model. Therefore, in this paper, it is expected that through further research, both the simple presence of calcium and the automation of the calcium analysis score for each coronary artery calcium score CT will become possible.
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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...