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Related Concept Videos

Aortic Regurgitation II: Clinical Features and Diagnostic Tests01:22

Aortic Regurgitation II: Clinical Features and Diagnostic Tests

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Aortic valve regurgitation (AR) occurs when the aortic valve fails to close properly, allowing blood to flow backward from the aorta into the left ventricle. This backflow can result in two distinct clinical presentations: acute and chronic AR, each characterized by its own set of symptoms and physical findings.Acute Aortic RegurgitationAcute AR presents with a sudden onset of severe symptoms. Patients typically experience profound dyspnea (shortness of breath), chest pain, and signs of left...
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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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IntroductionAortic regurgitation is characterized by the backward flow of blood from the aorta into the left ventricle during diastole and arises from the improper closure of the aortic valve. This condition results in left ventricular volume overload and can stem from both acute and chronic etiologies, each contributing uniquely to the disease's progression and symptomatology.Acute and Chronic CausesAcute aortic regurgitation often results from events that suddenly impair the integrity of the...
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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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Thoracic Aorta01:15

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The thoracic section of the aorta begins at the T5 vertebra and extends to the T12 level at the diaphragm, initially progressing through the mediastinum to the left of the spinal column. Throughout its course in the thoracic segment, the thoracic aorta emits various offshoots known collectively as visceral and parietal branches. The branches that predominantly supply blood to visceral organs are termed visceral branches and include bronchial, pericardial, esophageal, and mediastinal arteries,...
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Related Experiment Video

Updated: Jul 16, 2025

Four-Dimensional Computed Tomography-Guided Valve Sizing for Transcatheter Pulmonary Valve Replacement
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Aortic Annulus Detection Based on Deep Learning for Transcatheter Aortic Valve Replacement Using Cardiac Computed

Yongwon Cho1,2, Soojung Park1, Sung Ho Hwang3

  • 1Department of Radiology, Korea University Anam Hospital, Seoul, Korea.

Journal of Korean Medical Science
|September 19, 2023
PubMed
Summary

A novel deep learning model, ADPANet, effectively detects the aortic annulus plane in cardiac CT scans for transcatheter aortic valve replacement (TAVR). This automated approach shows promise for improving TAVR planning and outcomes.

Keywords:
Annulus PlaneCardiac Image AnalysisConvolutional Neural NetworkDeep Learning TAVR

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Area of Science:

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

Background:

  • Transcatheter aortic valve replacement (TAVR) requires precise anatomical assessment.
  • Accurate detection of the aortic annulus plane is crucial for TAVR success.
  • Current methods for annulus plane detection can be time-consuming and operator-dependent.

Purpose of the Study:

  • To propose and evaluate a deep learning architecture for automated aortic annulus plane detection.
  • To develop a novel method for complex 3D structure analysis using cardiac computed tomography (CT).
  • To enhance the efficiency and accuracy of pre-procedural planning for TAVR.

Main Methods:

  • A retrospective review of 72 patients undergoing TAVR was conducted.
  • The Annulus Detection Permuted AdaIN network (ADPANet), a 3D U-net based architecture, was developed.
  • The model was trained and tested on cardiac CT scans, with performance evaluated using RMSE and DSC.

Main Results:

  • ADPANet demonstrated feasibility in detecting the 3D aortic annulus plane.
  • The model achieved a Root Mean Square Error (RMSE) of 55.078 ± 35.794.
  • A Dice Similarity Coefficient (DSC) of 0.496 ± 0.217 was recorded, indicating moderate overlap.

Conclusions:

  • The developed deep learning framework is a feasible tool for detecting the complex aortic annulus plane in cardiac CT for TAVR.
  • ADPANet's performance suggests potential for improved accuracy compared to other convolutional neural networks.
  • This automated approach could streamline pre-TAVR assessments and potentially improve patient outcomes.