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Pulmonary Tuberculosis IV01:26

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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
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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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Machine and Deep Learning for Tuberculosis Detection on Chest X-Rays: Systematic Literature Review.

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Journal of Medical Internet Research
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Summary
This summary is machine-generated.

Artificial intelligence, including machine learning (ML) and deep learning (DL), shows high potential for detecting tuberculosis (TB) on chest X-rays (CXRs). Further research should focus on improving risk of bias assessment for more reliable diagnostic accuracy.

Keywords:
PRISMA guidelinesQUADAS-2chest x-raysconvolutional neural networksdiagnostic test accuracymachine and deep learningrisk of biassensitivity and specificitysystematic literature reviewtuberculosis detection

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Area of Science:

  • Medical Imaging
  • Artificial Intelligence in Medicine
  • Radiology

Background:

  • Tuberculosis (TB) is a leading infectious cause of mortality globally.
  • Chest radiography (CXR) plays a crucial role in TB detection and diagnosis.
  • Human interpretation of CXRs has significant inter-reader variability, impacting reliability.

Purpose of the Study:

  • To systematically review and assess the performance of machine learning (ML) and deep learning (DL) algorithms for TB detection using CXR.
  • To evaluate the diagnostic accuracy of AI-based methods compared to traditional human interpretation.

Main Methods:

  • Systematic literature review (SLR) following PRISMA guidelines.
  • Searched Scopus, PubMed, and IEEE databases for relevant studies.
  • Included 47 studies, performed risk of bias assessment (QUADAS-2), and conducted meta-analysis on 10 studies.

Main Results:

  • Deep learning (DL) was more prevalent (34 studies) than machine learning (ML) (7 studies).
  • ML achieved higher mean accuracy (~93.71%) and sensitivity (~92.55%), while DL showed better mean AUC (~92.12%) and specificity (~91.54%).
  • Pooled sensitivity and specificity for ML and DL methods were estimated at 0.9857 and 0.9805, respectively.

Conclusions:

  • Both ML and DL demonstrate significant potential for TB detection via CXR.
  • Future research should prioritize addressing risks of bias, particularly concerning the reference standard and study flow/timing.
  • Only two studies developed practical applications from their proposed AI solutions.