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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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Pulmonary Tuberculosis I01:29

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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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Pulmonary Tuberculosis II01:28

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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 III01:31

Pulmonary Tuberculosis III

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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.
The first classification is based on the development of the disease, and it includes the following categories:
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Radiological Investigation I: X-ray and CT01:30

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Radiological investigations, including X-rays and computed tomography (CT) scans, are critical for diagnosing and evaluating various medical conditions. These imaging techniques provide valuable insights into the body's internal structures, aiding in the detection of abnormalities, assessment of disease progression, and development of treatment strategies. This article delves into two primary radiological investigations, chest X-rays and CT scans, outlining their purpose, procedures, and...
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Pulmonary Tuberculosis V01:28

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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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AI-Assisted Tuberculosis Detection and Classification from Chest X-Rays Using a Deep Learning Normalization-Free

Vasundhara Acharya1, Gaurav Dhiman2,3,4, Krishna Prakasha5

  • 1Department of Computer Science and Engineering, Manipal Institute of Technology, Manipal Academy of Higher Education, Manipal 576104, India.

Computational Intelligence and Neuroscience
|October 13, 2022
PubMed
Summary
This summary is machine-generated.

Early detection of tuberculosis (TB) is crucial. This study introduces progressive resizing with AI-powered chest X-ray analysis, achieving high accuracy for TB diagnosis and aiding radiologists.

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Area of Science:

  • Medical Imaging
  • Artificial Intelligence
  • Infectious Diseases

Background:

  • Tuberculosis (TB) is a deadly airborne disease requiring early detection.
  • Limited resources in low-income countries necessitate efficient diagnostic tools.
  • Digital radiography (DR) and AI-powered computer-aided detection (CAD) offer practical solutions.

Purpose of the Study:

  • To develop an automated method for TB detection using chest X-ray images.
  • To enhance diagnostic accuracy and efficiency in TB screening.
  • To provide a supplementary decision tool for radiologists.

Main Methods:

  • Utilized progressive resizing for training AI models.
  • Employed ImageNet fine-tuned Normalization-Free Networks (NFNets) for classification.
  • Applied the Score-Cam algorithm for region highlighting in X-rays.

Main Results:

  • Achieved 96.91% accuracy, 99.38% AUC, 91.81% sensitivity, and 98.42% specificity in multiclass classification.
  • Reached 96% accuracy and 98% AUC for binary classification.
  • Demonstrated the method's effectiveness as a secondary diagnostic tool.

Conclusions:

  • The proposed AI-driven method significantly improves TB detection accuracy from chest X-rays.
  • This approach can assist radiologists in clinical settings, especially in resource-limited areas.
  • Automated TB diagnosis using AI shows promise for global public health initiatives.