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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.
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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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Diagnostic Performance of Artificial Intelligence-Based Methods for Tuberculosis Detection: Systematic Review.

Seng Hansun1,2, Ahmadreza Argha3,4,5, Ivan Bakhshayeshi3,6

  • 1School of Clinical Medicine, South West Sydney, UNSW Medicine & Health, UNSW Sydney, Sydney, Australia.

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This summary is machine-generated.

Artificial intelligence (AI) shows significant promise for tuberculosis (TB) detection, achieving high accuracy across various data types. Future research should focus on domain-shift analysis for real-world application.

Keywords:
AIPRISMAPreferred Reporting Items for Systematic Reviews and Meta-AnalysisQUADAS-2Quality Assessment of Diagnostic Accuracy Studies version 2artificial intelligencedeep learningdiagnostic performancemachine learningsystematic literature reviewtuberculosis detection

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Area of Science:

  • Medical Imaging
  • Machine Learning
  • Infectious Disease Diagnostics

Background:

  • Tuberculosis (TB) is a leading cause of infectious disease mortality globally.
  • Current diagnostic tools are insufficient for comprehensive TB detection.
  • Artificial intelligence (AI) offers potential solutions for improving TB diagnosis.

Purpose of the Study:

  • To systematically evaluate AI-based algorithms for TB detection.
  • To analyze AI performance across diverse data modalities and biomarkers.
  • To synthesize current knowledge on AI applications in TB diagnostics.

Main Methods:

  • Systematic review following PRISMA 2020 guidelines.
  • Searched Scopus, PubMed, and ACM Digital Library for relevant studies.
  • Included 152 studies after screening 1146 records.
  • Assessed risk of bias using QUADAS-2.

Main Results:

  • Radiographic biomarkers and deep learning (DL) were predominant methods.
  • Convolutional Neural Networks (CNNs) like VGG-16, ResNet-50, and DenseNet-121 were common.
  • AI methods demonstrated high performance: mean accuracy 91.93%, mean AUC 93.48%.
  • Transfer learning (TL) showed increasing popularity.
  • Only one study performed domain-shift analysis.

Conclusions:

  • AI-based methods show considerable promise for TB detection.
  • Further research should include domain-shift analyses to enhance real-world applicability.
  • AI can significantly improve the diagnostic pathway for TB.