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Related Concept Videos

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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Imaging Studies for Cardiovascular System V: CT01:28

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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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Validation of artificial intelligence-based quantitative coronary angiography.

Do-Hyun Kim1, Sun-Hwa Kim1, Hyun-Wook Chu1,2

  • 1Cardiovascular Center, Seoul National University Bundang Hospital, Seongnam-si, Korea.

Digital Health
|December 19, 2024
PubMed
Summary

Artificial intelligence-based quantitative coronary angiography (AI-QCA) shows high accuracy and consistency compared to manual QCA. This AI tool offers rapid, real-time analysis, potentially improving coronary artery disease diagnosis and treatment.

Keywords:
Coronary artery diseasedeep learningexternal validationobject detectionquantitative coronary angiography

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Area of Science:

  • Cardiology
  • Medical Imaging
  • Artificial Intelligence

Background:

  • Coronary angiography is crucial for diagnosing and treating coronary artery disease.
  • Manual quantitative coronary angiography (QCA) is accurate but time-consuming.
  • Artificial intelligence (AI) offers automated, rapid medical image analysis for real-time quantitative coronary analysis.

Purpose of the Study:

  • To evaluate the accuracy of AI-based QCA (AI-QCA) against manual QCA.
  • To assess clinician acceptance of AI-QCA.
  • To compare AI-QCA with visual estimation in a pilot study.

Main Methods:

  • A retrospective, single-center study in two phases.
  • Phase 1: Pilot study (15 patients) comparing AI-QCA, manual QCA, and visual estimation.
  • Phase 2: Larger cohort (762 patients, 1002 angiograms) analyzing AI-QCA vs. manual QCA.

Main Results:

  • AI-QCA demonstrated superior consistency over visual estimation in Phase 1.
  • A strong correlation was found between AI-QCA and manual QCA in Phase 2.
  • AI-QCA accurately analyzed lesions in major vessels, comparable to manual QCA.

Conclusions:

  • AI-QCA shows high concordance with manual QCA.
  • AI-QCA provides real-time analysis, reducing workload.
  • AI-QCA is a promising tool for coronary artery disease diagnosis and treatment, requiring further clinical validation.