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Related Concept Videos

Imaging Studies for Cardiovascular System IV: CMRI01:21

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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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The most common cardiovascular diagnostic test is an X-ray. It produces images of the heart, blood vessels, and adjacent structures.
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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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Related Experiment Video

Updated: Mar 8, 2026

Detection of Architectural Distortion in Prior Mammograms via Analysis of Oriented Patterns
13:44

Detection of Architectural Distortion in Prior Mammograms via Analysis of Oriented Patterns

Published on: August 30, 2013

43.8K

Detecting Cardiovascular Disease from Mammograms With Deep Learning.

Juan Wang, Huanjun Ding, Fatemeh Azamian Bidgoli

    IEEE Transactions on Medical Imaging
    |January 24, 2017
    PubMed
    Summary
    This summary is machine-generated.

    Automated detection of breast arterial calcifications (BACs) in mammograms using deep learning shows accuracy comparable to human experts. This technology can aid in assessing cardiovascular risk in women by identifying BACs from mammograms.

    Related Experiment Videos

    Last Updated: Mar 8, 2026

    Detection of Architectural Distortion in Prior Mammograms via Analysis of Oriented Patterns
    13:44

    Detection of Architectural Distortion in Prior Mammograms via Analysis of Oriented Patterns

    Published on: August 30, 2013

    43.8K

    Area of Science:

    • Radiology and Medical Imaging
    • Artificial Intelligence in Healthcare
    • Cardiovascular Disease Research

    Background:

    • Coronary artery disease (CAD) is a leading cause of mortality in women.
    • Breast arterial calcifications (BACs) visible on mammograms are associated with CAD.
    • Accurate detection of BACs can improve cardiovascular risk assessment.

    Purpose of the Study:

    • To evaluate the feasibility of an automated deep learning system for detecting BACs in mammograms.
    • To compare the performance of the automated system against human expert radiologists.
    • To assess the accuracy of the system in quantifying arterial calcification mass.

    Main Methods:

    • Development of a 12-layer convolutional neural network (CNN) for BAC detection.
    • Application of a pixelwise, patch-based procedure for BAC identification.
    • Reader study involving expert radiologists to establish ground truth.
    • Performance evaluation using 840 digital mammograms (210 cases) with FROC analysis and calcium mass quantification.

    Main Results:

    • The deep learning system achieved a detection performance comparable to human experts.
    • Calcium mass quantification showed high correlation with ground truth (R-squared = 96.24%).
    • The automated system demonstrated effectiveness in identifying and quantifying BACs.

    Conclusions:

    • Deep learning offers a viable approach for automated BAC detection in mammograms.
    • This technology can serve as a valuable tool for cardiovascular risk stratification in women.
    • Automated BAC detection may enhance early identification of patients at risk for CAD.