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September 10, 2025The Lancet Digital HealthOpen Access

Computer-aided reading of chest radiographs for paediatric tuberculosis: current status and future directions

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Authors

MDM. G. DupontRCRobert CastroSKSandra V. Kik

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Overview

Observational analysis reveals gaps in CAD for paediatric tuberculosis, highlighting data needs for effective detection.

Key Points

  • Detecting paediatric tuberculosis through CAD systems is crucial, as current models primarily focus on adults.
  • Large paediatric CXR datasets are essential to develop effective CAD models that recognize unique child-specific conditions.
  • Transfer learning has shown promise in improving CAD algorithms for reading paediatric chest x-rays.
  • Improving CAD equity for children's tuberculosis can significantly reduce the global burden of the disease.

Cite This Study

Dupont et al. (2025) studied this question.

synapsesocial.com/papers/68c1d23a54b1d3bfb60f8017https://doi.org/10.1016/j.landig.2025.100884
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Also Consider

Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context:

  1. 1Computer-aided detection for radiological disease severity classification on chest radiograph in children with intra-thoracic tuberculosis2026
  2. 2Catalysing Artificial Intelligence for Paediatric Tuberculosis Research (CAPTURE): protocol for a global multicentre study establishing a paediatric chest X-ray repository to evaluate computer-aided detection algorithms2026
  3. 3Accuracy of<b><i>Computer-Aided Detection for Tuberculosis</i></b>® on paediatric chest radiographs2024 · 4 citations
  4. 4Computer-aided detection of TB from chest radiographs in a TB prevalence survey: external validation and modelled impacts of commercially available artificial intelligence software2025
  5. 5An independent, multi-country head-to-head accuracy comparison of automated chest x-ray algorithms for the triage of pulmonary tuberculosis2024 · 6 citations