Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Related Concept Videos

Imaging Studies for Cardiovascular System VI: Calcium -Scoring CT

Imaging Studies for Cardiovascular System VI: Calcium -Scoring CT

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...

Imaging Studies for Cardiovascular System V: CT

Imaging Studies for Cardiovascular System V: CT

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...

Acute Coronary Syndrome III: Diagnostic Studies

Acute Coronary Syndrome III: Diagnostic Studies

Diagnosing acute coronary syndrome or ACS begins with a thorough patient history. Notable symptoms include central, crushing chest pain radiating to the left arm, neck, jaw, or back, along with shortness of breath, sweating (diaphoresis), nausea, vomiting, dizziness, and palpitations.It is crucial to note any history of cardiac illnesses and assess risk factors, including age, gender, smoking, hypertension, diabetes, hyperlipidemia, and a sedentary lifestyle.During physical examination, vital...

Imaging Studies for Cardiovascular System III: X-Ray

Imaging Studies for Cardiovascular System III: X-Ray

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...

Computed Tomography

Computed Tomography

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.
The technique was invented in the 1970s and is based on the principle that as X-rays pass through the body, they are absorbed or reflected at different levels. In the technique, a patient lies on a motorized platform while a computerized axial tomography (CAT) scanner rotates...

Imaging Studies for Cardiovascular System IV: CMRI

Imaging Studies for Cardiovascular System IV: CMRI

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,...

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by

Same author

Comparing incidence of heart failure in individuals with enlarged cardiac chambers versus diabetes.

American journal of preventive cardiology·2026

Same author

AI-quantified Myosteatosis at CAC CT for Prediction of Atrial Fibrillation and Heart Failure: The Multi-Ethnic Study of Atherosclerosis.

Radiology. Cardiothoracic imaging·2026

Same author

Self-enhancement-based perfusion mapping with contrast-enhanced magnetic resonance imaging for PAD assessment.

Magnetic resonance imaging·2026

Same author

A Systematic Review and Meta-Analysis of Breast Arterial Calcification and Its Association With Cardiovascular Disease and All-Cause Mortality.

The American journal of cardiology·2026

Same author

Artificial Intelligence-derived Measurements of Myosteatosis from Coronary Artery Calcium CT Scans to Predict COPD: The Multi-Ethnic Study of Atherosclerosis.

Radiology. Cardiothoracic imaging·2026

Same author

Developing and Validating Machine Learning-Driven Risk Indices to Predict Patient Dropout During Referral, Evaluation, and Waitlisting for Kidney Transplant.

Clinical transplantation·2025

Search research articles

Related Experiment Video

Updated: Jun 15, 2026

Identifying Coronary Artery Calcification on Non-gated Computed Tomography Scans

Identifying Coronary Artery Calcification on Non-gated Computed Tomography Scans

Published on: August 28, 2018

A supervised classification-based method for coronary calcium detection in non-contrast CT.

Uday Kurkure¹, Deepak R Chittajallu, Gerd Brunner

¹Computational Biomedicine Lab, Department of Computer Science, University of Houston, Houston, TX 77204, USA. ukurkure@uh.edu

The International Journal of Cardiovascular Imaging

|March 16, 2010

Summary

This summary is machine-generated.

Automated detection of coronary artery calcium using a novel hierarchical classifier significantly improves accuracy. This method aids in assessing atherosclerosis disease and cardiovascular risk more efficiently.

More Related Videos

Semi-Automatic Graphical Tool for Measuring Coronary Artery Spatially Weighted Calcium Score from Gated Cardiac Computed Tomography Images

Semi-Automatic Graphical Tool for Measuring Coronary Artery Spatially Weighted Calcium Score from Gated Cardiac Computed Tomography Images

Published on: September 22, 2023

Related Experiment Videos

Last Updated: Jun 15, 2026

Identifying Coronary Artery Calcification on Non-gated Computed Tomography Scans

Identifying Coronary Artery Calcification on Non-gated Computed Tomography Scans

Published on: August 28, 2018

Semi-Automatic Graphical Tool for Measuring Coronary Artery Spatially Weighted Calcium Score from Gated Cardiac Computed Tomography Images

Semi-Automatic Graphical Tool for Measuring Coronary Artery Spatially Weighted Calcium Score from Gated Cardiac Computed Tomography Images

Published on: September 22, 2023

Area of Science:

Cardiology
Medical Imaging
Artificial Intelligence

Background:

Accurate quantification of coronary artery calcium is crucial for assessing atherosclerosis and cardiovascular risk.
Current manual detection methods are labor-intensive and time-consuming.
The heterogeneous nature of candidate regions presents a challenge for automated detection.

Purpose of the Study:

To develop an automated tool for detecting and quantifying coronary artery calcium.
To overcome the challenges posed by diverse and heterogeneous candidate regions.
To reduce observer burden in coronary calcification assessment.

Main Methods:

Investigated a supervised classification-based approach for automated coronary calcium detection.
Proposed a two-stage, hierarchical classifier using ensembles of cost-sensitive learners.
Utilized relative location and neighboring region properties for improved discrimination.

Main Results:

Achieved high performance on a testing dataset of 105 subjects' non-contrast computed tomography scans.
Reported detection accuracy of 98.27%, sensitivity of 92.07%, and specificity of 98.62%.
Demonstrated the effectiveness of the hierarchical classifier in distinguishing coronary calcifications.

Conclusions:

The proposed automated method effectively detects coronary calcifications.
This approach offers a more efficient alternative to manual identification.
The findings support the use of AI in cardiovascular risk assessment.