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Related Concept Videos

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 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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Identifying Coronary Artery Calcification on Non-gated Computed Tomography Scans
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Deep learning-based stenosis quantification from coronary CT Angiography.

Youngtaek Hong1,2, Frederic Commandeur2, Sebastien Cadet3

  • 1Brain Korea 21 Project for Medical Science, Yonsei University, Seoul, Republic of Korea.

Proceedings of Spie--The International Society for Optical Engineering
|November 26, 2019
PubMed
Summary
This summary is machine-generated.

Deep learning accurately quantifies coronary artery disease from computed tomography angiography (CTA) by measuring minimal luminal area, diameter stenosis, and contrast density difference. This automated approach shows excellent correlation with expert readers, enhancing clinical reporting.

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Area of Science:

  • Cardiovascular Imaging
  • Artificial Intelligence in Medicine
  • Medical Image Analysis

Background:

  • Coronary computed tomography angiography (CTA) enables stenosis quantification, but this is not standard clinical practice.
  • Current quantitative analysis of coronary artery disease (CAD) from CTA is limited in routine clinical application.

Purpose of the Study:

  • To evaluate the feasibility of using deep learning for accurate quantification of coronary artery disease from CTA.
  • To assess the performance of a deep learning model in measuring key stenosis parameters compared to expert readers.

Main Methods:

  • Analysis of 716 diseased segments from 156 patients who underwent CTA.
  • Measurement of minimal luminal area (MLA), percent diameter stenosis (DS), and percent contrast density difference (CDD) using semi-automated software by an expert.
  • Development of a deep learning model with convolutional neural networks for lumen and plaque segmentation, validated using 10-fold cross-validation.

Main Results:

  • Excellent correlation was observed between deep learning and expert reader measurements for MLA (r=0.984), DS (r=0.957), and CDD (r=0.975) (p<0.001 for all).
  • No significant differences were found for MLA and CDD between deep learning and expert readers (p=0.68 and p=0.30, respectively).
  • While a significant difference was noted for DS (p<0.05), all quantitative measure ranges fell within inter-observer variability.

Conclusions:

  • The developed deep learning-based method accurately quantifies coronary artery disease segments from CTA.
  • This automated approach has the potential to significantly enhance the clinical reporting of CAD from CTA.
  • Deep learning offers a feasible and accurate tool for quantitative analysis in cardiovascular imaging.