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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.
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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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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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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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Related Experiment Video

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Author Spotlight: Optimizing CFU Determination for Efficient Assessment of TB Vaccine Efficacy and Antigen Presentation Analysis
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BacilliFinder: Revolutionizing Tuberculosis Detection with Computer Vision.

Nagaraju Y1, Venkatesh1, Rajani G2

  • 1Department of Computer Science and Engineering, University Visvesvaraya College of Engineering, Bangalore University, Bengaluru, India.

The Indian Journal of Tuberculosis
|December 8, 2025
PubMed
Summary

A new deep learning model, YOLOv7_Lite, enhances tuberculosis detection from sputum smear images. This computationally efficient model improves accuracy and sensitivity for early diagnosis, especially on edge devices.

Keywords:
Automated smear microscopyBacilliDeep learningDown-scaled modelMycobacterium tuberculosisObject detectionYolov7Yolov7_Lite

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Area of Science:

  • Medical Imaging
  • Artificial Intelligence
  • Microbiology

Background:

  • Tuberculosis (TB) diagnosis relies on sputum smears and X-rays, which have limitations in accuracy and require skilled personnel.
  • Current diagnostic methods for Mycobacterium bacilli can be subjective and lack sensitivity, impacting early detection rates.
  • Global TB burden necessitates improved, accessible diagnostic tools.

Purpose of the Study:

  • To develop an efficient, computationally light deep convolutional model for Mycobacterium detection in sputum smears.
  • To enable TB diagnosis on edge devices with limited computational resources.
  • To improve the accuracy and robustness of microscopic TB detection.

Main Methods:

  • A downscaled deep convolutional neural network (YOLOv7_Lite) was designed for efficiency.
  • A custom dataset of sputum smear images was curated for model training and validation.
  • The model's performance was evaluated using metrics like mean Average Precision (mAP), recall, and precision.

Main Results:

  • The YOLOv7_Lite model achieved high performance, with an 87.2% mAP at an IoU threshold of 0.5.
  • The model demonstrated strong detection capabilities across varying IoU thresholds (50.5% mAP from 0.5 to 0.95).
  • Excellent recall (86%) and precision (84%) metrics were recorded, indicating robust diagnostic potential.

Conclusions:

  • The YOLOv7_Lite model offers a stable and sensitive solution for Mycobacterium detection in sputum smears.
  • Its computational efficiency and adaptability make it suitable for deployment on memory-constrained edge devices.
  • This approach can significantly enhance early and accurate tuberculosis diagnosis by improving smear microscopy sensitivity.