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Related Concept Videos

Pulmonary Tuberculosis IV01:26

Pulmonary Tuberculosis IV

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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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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.
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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.
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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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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.
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Ensemble deep learning architectures for detecting pulmonary tuberculosis in chest X-rays.

Alba García Seco de Herrera1, Ekin Yagis2, Nichapat Pinpo3

  • 1UNED, Madrid, Spain. alba.garcia@lsi.uned.es.

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Summary

This study introduces an automated tuberculosis (TB) screening tool using AI for chest X-rays. The cost-effective method improves TB detection in under-resourced areas, aiding early diagnosis.

Keywords:
Chest radiographyConvolutional neural networks (CNNs)Ensemble deep learningMedical image analysisPulmonary tuberculosis detectionRespiratory disease screening

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Area of Science:

  • Artificial Intelligence
  • Medical Imaging
  • Public Health

Background:

  • Tuberculosis (TB) is a significant global health issue, causing millions of deaths annually.
  • Limited access to expert radiological interpretation hinders timely TB diagnosis in high-burden regions.
  • There is a need for cost-effective, automated screening tools for TB detection in under-resourced settings.

Purpose of the Study:

  • To develop and evaluate a novel, automated TB screening method using an ensemble learning architecture.
  • To provide a cost-effective solution for TB detection from chest radiographs suitable for low-resource environments.
  • To enhance the accuracy and reliability of TB diagnosis in areas with limited radiological expertise.

Main Methods:

  • An ensemble learning architecture integrating a Convolutional Autoencoder Neural Network and a Multi-Scale Convolutional Neural Network with deep layer aggregation was developed.
  • The framework was trained and validated on multiple public and private datasets of chest radiographs.
  • Performance was assessed using metrics such as sensitivity, specificity, and Area Under the Receiver Operating Characteristic (AUROC) curve.

Main Results:

  • The proposed method achieved high diagnostic performance, including 99% sensitivity and 94% specificity on the Shenzhen dataset.
  • Consistent high accuracy was observed across all evaluated datasets, demonstrating robust generalisability.
  • The ensemble model outperformed existing classifiers, achieving a state-of-the-art AUROC of 0.98.
  • Expert radiologists confirmed the clinical relevance and diagnostic reliability of the AI-driven predictions.

Conclusions:

  • The developed ensemble learning approach presents a practical and scalable tool for automated TB screening from chest X-rays.
  • This AI-driven method has the potential to significantly improve TB detection rates, especially in low- and middle-income countries.
  • The findings support the clinical utility of advanced AI in addressing global health challenges like tuberculosis.