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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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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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Automated Aortic Quantification Based on Artificial Intelligence: Validation Using Contrast-Enhanced and Non-Contrast

Jia-Sheng Hong1, Yun-Hsuan Tzeng2,3, Kuan-Ting Wu1,3

  • 1Institute of Biophotonics, National Yang Ming Chiao Tung University, Taipei 112, Taiwan.

Bioengineering (Basel, Switzerland)
|May 4, 2026
PubMed
Summary
This summary is machine-generated.

An artificial intelligence (AI) method accurately quantifies the aorta in CT scans for early detection of aortic dilatation. This AI tool shows high sensitivity and agreement with manual measurements, aiding clinical intervention.

Keywords:
3D SlicerTotalSegmentatoraortic dilationartificial intelligenceautomated aortic quantificationcomputed tomographycontrast-enhanced CTnon-contrast CTopen-source workflow

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Area of Science:

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

Background:

  • Early detection of aortic dilatation is crucial for preventing severe aortic disease.
  • Timely intervention based on accurate aortic measurements can improve patient outcomes.
  • Current methods for aortic quantification may be time-consuming or lack standardization.

Purpose of the Study:

  • To develop and validate an artificial intelligence (AI) method for automated aortic quantification.
  • To assess the AI's performance on both contrast-enhanced and non-contrast CT scans.
  • To support early detection of aortic dilatation and facilitate timely clinical intervention.

Main Methods:

  • Utilized open-source tools to develop an AI algorithm for aortic measurement.
  • Analyzed 380 paired contrast-enhanced and non-contrast CT scans from 190 patients.
  • Validated AI-derived measurements against manual annotations and clinical records.

Main Results:

  • The AI demonstrated strong agreement with manual annotations (correlation coefficients up to 0.987).
  • High sensitivity for AI detection of aortic dilatation was observed: 97% for contrast-enhanced CT and 94% for non-contrast CT.
  • The AI workflow showed excellent performance across different imaging modalities.

Conclusions:

  • The developed AI-based workflow enables highly sensitive and automated aortic quantification.
  • This method is effective for both contrast-enhanced and non-contrast CT scans, increasing clinical applicability.
  • The AI tool supports early detection of aortic dilatation, potentially improving patient management.