A Comprehensive Review of the Diagnostics for Pediatric Tuberculosis Based on Assay Time, Ease of Operation, and

Soumya Basu1, Subhra Chakraborty1

  • 1Department of International Health, Bloomberg School of Public Health, Johns Hopkins University, Baltimore, MD 21205, USA.

Microorganisms
|January 25, 2025
PubMed

Insights

Diagnosing pediatric tuberculosis (TB) in low-resource settings faces challenges. This review explores rapid diagnostic methods, including molecular and AI techniques, to improve early detection and patient outcomes.

Area of Science:

  • Public Health
  • Infectious Diseases
  • Medical Diagnostics

Background:

  • Pediatric tuberculosis (TB) presents significant diagnostic challenges, particularly in low- and middle-income countries (LMICs).
  • Diagnostic turnaround time (TAT) and operational simplicity are crucial for timely treatment initiation, impacting pediatric TB morbidity and mortality.
  • Existing diagnostic methods often struggle to meet the demands of resource-limited settings.

Purpose of the Study:

  • To review the evolving landscape of pediatric TB diagnostics, focusing on turnaround time and ease of operation.
  • To evaluate traditional, molecular, and emerging biomarker-based diagnostic approaches for TB in children.
  • To identify gaps and challenges in current diagnostic strategies for pediatric TB in resource-limited environments.

Main Methods:

  • Literature review of diagnostic methods for pediatric tuberculosis.
  • Analysis of diagnostic techniques based on turnaround time (TAT) and operational feasibility.
  • Examination of traditional methods (microscopy, culture), molecular techniques, biomarker-based approaches, and AI-driven detection.

Main Results:

  • Rapid molecular and AI-based methods show promise for improved speed and sensitivity in pediatric TB diagnosis.
  • Trade-offs between rapid results, accuracy, and clinical utility require careful consideration.
  • Implementation challenges in resource-limited settings, including cost and algorithm gaps, persist.

Conclusions:

  • Optimizing pediatric TB diagnostic strategies requires balancing speed, performance, cost, and feasibility.
  • Emerging technologies like AI offer potential advancements but face implementation hurdles.
  • Further development and strategic implementation are needed to address the global burden of pediatric TB.

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