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Related Concept Videos

Pulmonary Tuberculosis I01:29

Pulmonary Tuberculosis I

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...
Pulmonary Tuberculosis III01:31

Pulmonary Tuberculosis III

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:
Pulmonary Tuberculosis IV01:26

Pulmonary Tuberculosis IV

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...
Pulmonary Tuberculosis II01:28

Pulmonary Tuberculosis II

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...
Pulmonary Tuberculosis V01:28

Pulmonary Tuberculosis V

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 progression...

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Related Experiment Videos

Federated learning for privacy-preserving multi-center tuberculosis diagnosis using chest imaging data.

Srijita Bhattacharjee1, Vinod Sapkal2, Varun Kumar Sharma3

  • 1Department of Information Technology, Pillai HOC College of Engineering and Technology, Rasayani, Maharashtra, India.

The Indian Journal of Tuberculosis
|July 15, 2026
PubMed
Summary

Federated learning enables multiple institutions to train a shared deep learning model for tuberculosis (TB) diagnosis using chest X-rays without sharing patient data. This privacy-preserving approach achieves high accuracy, improving global health surveillance.

Keywords:
Chest imagingDeep learningFederated learningMulti-center dataPrivacy-preserving AITuberculosis diagnosis

Related Experiment Videos

Area of Science:

  • Medical Imaging Analysis
  • Artificial Intelligence in Healthcare
  • Privacy-Preserving Machine Learning

Background:

  • Tuberculosis (TB) poses a significant global health threat, necessitating improved diagnostic tools.
  • Chest X-rays are crucial for TB screening, but interpretation is subjective and requires expert radiologists.
  • Centralized data analysis for medical imaging raises privacy concerns and regulatory challenges (e.g., HIPAA, GDPR).

Purpose of the Study:

  • To develop and evaluate a federated learning framework for multi-center, privacy-preserving tuberculosis diagnosis using chest imaging.
  • To enable collaborative model training across institutions without compromising patient data confidentiality.
  • To assess the diagnostic performance and generalization capabilities of the federated approach.

Main Methods:

  • Implementation of a federated learning framework utilizing convolutional neural networks (CNNs) for TB detection.
  • Integration of privacy-enhancing techniques: differential privacy, secure aggregation, and encryption.
  • Collaborative training across multiple healthcare institutions using local datasets (Shenzhen, Montgomery, NIH ChestX-ray14).
  • Evaluation of hybrid CNN-transformer architectures for enhanced interpretability.

Main Results:

  • The federated CNN model achieved high diagnostic accuracy (94.8%), sensitivity (93.5%), and specificity (95.2%).
  • Performance closely matched centralized training models while demonstrating superior generalization across diverse datasets.
  • Hybrid architectures showed potential for improved interpretability and precision in TB diagnosis.

Conclusions:

  • Federated learning offers an effective solution for multi-institutional medical imaging analysis, balancing diagnostic accuracy with robust patient privacy.
  • The proposed framework establishes a scalable, secure, and collaborative paradigm for disease diagnosis and healthcare data governance.
  • This approach facilitates broader applications in collaborative medical research and public health initiatives.