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Related Concept Videos

Pulmonary Tuberculosis III01:31

Pulmonary Tuberculosis III

365
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:
365
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 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 I01:29

Pulmonary Tuberculosis I

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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 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.
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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Pneumothorax-II01:27

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Pneumothorax is a medical condition defined by the buildup of air in the pleural space between the lungs and the chest wall. This accumulation of air can lead to partial or complete lung collapse, resulting in a range of clinical manifestations. Understanding the clinical presentation and effective management strategies is crucial for healthcare professionals in providing timely and appropriate care to individuals with pneumothorax.
Clinical Manifestations:
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Deep Learning Classification of Tuberculosis Chest X-rays.

Kartik K Goswami1, Rakesh Kumar2, Rajesh Kumar3

  • 1College of Medicine, California Northstate University College of Medicine, Elk Grove, USA.

Cureus
|August 10, 2023
PubMed
Summary
This summary is machine-generated.

A new machine learning model accurately detects tuberculosis (TB) using chest X-rays, achieving 94% accuracy. This advancement aids in early diagnosis and treatment of the infectious disease.

Keywords:
chest x-ray (cx-ray)diseaseimaginginfectious diseaseinternal medicinemedicinepulmonologytbtuberculosisx-ray

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Area of Science:

  • Medical Imaging
  • Machine Learning
  • Infectious Diseases

Background:

  • Tuberculosis (TB) is a significant global health challenge caused by Mycobacterium tuberculosis.
  • It primarily affects the lungs but can impact other organs, spreading via airborne droplets.
  • Drug resistance, HIV co-infection, and resource limitations hinder TB eradication efforts.

Purpose of the Study:

  • To develop a machine learning model for detecting tuberculosis (TB) using chest X-ray images.
  • To train a model for efficient recognition of TB patterns in radiographic images.
  • To facilitate timely diagnosis and treatment of TB.

Main Methods:

  • Utilized over 1196 chest X-ray images from Kaggle.com.
  • Developed and trained a machine learning model on Google's Collaboration Platform.
  • Employed supervised learning to classify TB-positive and normal chest X-rays.

Main Results:

  • The model achieved 94% overall accuracy in TB detection.
  • Demonstrated high performance with precision and recall of 94.1% each.
  • Reported sensitivity of 96.85% and specificity of 91.49%, with an F1 score of 0.941.

Conclusions:

  • Machine learning shows significant potential for accurate TB detection from chest X-rays.
  • Further validation is required to assess generalizability and clinical integration.
  • The model can contribute to earlier TB detection and improved patient management.