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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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Acute Coronary Syndrome III: Diagnostic Studies01:30

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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...
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Coronary Artery Disease I: Introduction01:30

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Coronary Artery Disease (CAD): An Overview with Scientific InsightsCoronary Artery Disease (CAD), often referred to as C-A-D, is a prevalent blood vessel disorder classified under the broader category of atherosclerosis. Atherosclerosis is a pathological process characterized by the hardening and narrowing of arteries due to the accumulation of atherosclerotic plaques. These plaques are composed of cholesterol, fatty substances, inflammatory cells, calcium, and fibrin, reducing blood flow to...
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Coronary Artery Disease II: Pathophysiology01:26

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Coronary Artery Disease (CAD) originates from a series of events that impair the function of coronary arteries, the blood vessels responsible for delivering oxygen-rich blood to the heart muscle. The pathophysiology of CAD is closely linked to atherosclerosis, a chronic inflammatory and lipid-driven condition affecting the vascular endothelium.1. Endothelial DamageThe process begins with damage to the vascular endothelium, which serves as a protective barrier between the blood and the vessel...
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Imaging Studies for Cardiovascular System III: X-Ray01:20

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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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Identifying Coronary Artery Calcification on Non-gated Computed Tomography Scans
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Deep learning-based coronary computed tomography analysis to predict functionally significant coronary artery

Manami Takahashi1, Reika Kosuda2, Hiroyuki Takaoka3

  • 1Department of Cardiovascular Medicine, Chiba University Graduate School of Medicine, 1-8-1 Inohana, Chuo-ku, Chiba, Japan.

Heart and Vessels
|August 8, 2023
PubMed
Summary

Deep learning (DL) analysis of coronary CT data improves prediction of invasive fractional flow reserve (FFR), especially in calcified arteries. This AI approach offers higher diagnostic accuracy than visual assessment for detecting significant coronary artery stenosis.

Keywords:
Computed tomographyDeep learningFractional flow reserve

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Area of Science:

  • Cardiology
  • Radiology
  • Artificial Intelligence

Background:

  • Fractional flow reserve derived from coronary CT (FFR-CT) is a noninvasive method correlating with invasive FFR.
  • FFR-CT accuracy can be limited by artifacts in severely calcified coronary arteries.

Purpose of the Study:

  • To evaluate the utility of deep learning (DL)-based coronary CT data analysis in predicting invasive fractional flow reserve (FFR).
  • To assess DL model performance specifically in cases with severely calcified coronary arteries.

Main Methods:

  • A deep neural network was trained using coronary CT images from 184 patients (241 coronary arteries).
  • The DL model's diagnostic accuracy for functionally significant stenosis (FFR < 0.80) was compared to visual assessment.

Main Results:

  • The DL model achieved a superior area under the curve (AUC) of 0.756 compared to visual assessment (0.574, P=0.011).
  • DL model demonstrated significantly higher sensitivity (82% vs 36%) and negative predictive value (87% vs 69%) for detecting FFR-positive stenosis.

Conclusions:

  • DL-based coronary CT data analysis shows higher diagnostic accuracy for functionally significant coronary artery stenosis than visual assessment.
  • DL models show promise in overcoming limitations of traditional FFR-CT analysis in calcified arteries.