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Related Concept Videos

Pulmonary Tuberculosis IV01:26

Pulmonary Tuberculosis IV

224
Tuberculosis, more commonly referred to as TB, is an infectious disease stemming from Mycobacterium tuberculosis. While it primarily impacts the lungs, TB can also affect other body areas. Given its severity and global impact, timely and accurate diagnosis is crucial for controlling its spread and improving patient outcomes.
Several diagnostic approaches are used to detect TB. The conventional method is the Tuberculin Skin Test (TST), also known as the Mantoux test. However, this method has...
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Pulmonary Tuberculosis II01:28

Pulmonary Tuberculosis II

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Tuberculosis, or TB, is a bacterial infectious disease caused by Mycobacterium tuberculosis. While its primary impact is on the lungs, leading to pulmonary tuberculosis, it can also affect various other organs, a condition referred to as extrapulmonary tuberculosis.
Here is a detailed explanation of its pathophysiology:
Transmission: The process begins when a person inhales droplet nuclei containing M. tuberculosis. These are typically released into the air when an individual with pulmonary or...
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Pulmonary Tuberculosis V01:28

Pulmonary Tuberculosis V

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Medical management of tuberculosis (TB) patients involves a comprehensive approach that includes diagnosis, treatment, and monitoring. The specific strategies can vary depending on the type of tuberculosis (latent or active), the patient's overall health status, and other considerations.
Latent tuberculosis infection occurs when TB bacteria are present in a person's body, but are not causing illness or symptoms. It is not contagious, and preventive treatment is crucial to avoid the...
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Pulmonary Tuberculosis I01:29

Pulmonary Tuberculosis I

389
Tuberculosis, often called TB, is a contagious illness primarily caused by Mycobacterium tuberculosis. It mainly affects the lung parenchyma but can also impact other body parts.
Causative Organism
The primary infectious agent causing tuberculosis is Mycobacterium tuberculosis, a slow-growing, acid-fast, aerobic rod that exhibits sensitivity to heat and ultraviolet light. Instances of Mycobacterium bovis and Mycobacterium avium contributing to the development of TB infection are rare.
Mode of...
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Pulmonary Tuberculosis III01:31

Pulmonary Tuberculosis III

503
Tuberculosis (TB) is a contagious infection primarily affecting the lung parenchyma but which can also affect other body parts. TB can be classified based on disease development, presentation, and the affected anatomical site.
The first classification is based on the development of the disease, and it includes the following categories:
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Related Experiment Video

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Analysis of 18FDG PET/CT Imaging as a Tool for Studying Mycobacterium tuberculosis Infection and Treatment in Non-human Primates
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TBNet: a context-aware graph network for tuberculosis diagnosis.

Si-Yuan Lu1, Shui-Hua Wang2, Xin Zhang3

  • 1School of Computing and Mathematical Sciences, University of Leicester, Leicester, LE1 7RH, UK.

Computer Methods and Programs in Biomedicine
|December 27, 2021
PubMed
Summary
This summary is machine-generated.

A new context-aware graph neural network, TBNet, accurately detects tuberculosis (TB) from chest CT scans. This AI tool aids radiologists by providing an effective verification for manual screening of this infectious disease.

Keywords:
computed tomographycomputer-aided diagnosisgraph neural networkrandom vector functional-link nettuberculosis

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Area of Science:

  • Medical Imaging
  • Artificial Intelligence
  • Graph Neural Networks

Background:

  • Tuberculosis (TB) is a common infectious bacterial disease affecting the lungs.
  • Computed tomography (CT) is crucial for lung imaging, but manual screening is burdensome and prone to variance.
  • Automated TB diagnosis systems are needed to assist radiologists.

Purpose of the Study:

  • To propose a novel context-aware graph neural network (TBNet) for automatic TB detection from chest CT images.
  • To leverage spatial relationships between image features for improved classification accuracy.

Main Methods:

  • Developed TBNet, a context-aware graph neural network.
  • Utilized feature graphs to generate context-aware features from CT images.
  • Employed a context-aware random vector functional-link net as the classifier.

Main Results:

  • TBNet achieved state-of-the-art performance in detecting TB from healthy samples.
  • The model demonstrated effectiveness in classifying chest CT images.

Conclusions:

  • TBNet serves as an accurate and effective tool for verifying manual screening in clinical TB diagnosis.
  • The proposed method enhances the efficiency and reliability of TB detection.