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Accuracy of CAD4TB (Computer-Aided Detection for Tuberculosis) on paediatric chest radiographs

Victory Fabian Edem1,2,3, Esin Nkereuwem1,4,3, Schadrac C Agbla5,6

  • 1Vaccines and Immunity Theme, MRC Unit The Gambia at London School of Hygiene and Tropical Medicine, Fajara, The Gambia.

The European Respiratory Journal
|September 3, 2024
PubMed

Insights

Computer-aided detection for tuberculosis (CAD4TBv7) shows high specificity but low sensitivity in detecting TB in children. Further optimization is crucial for improving pediatric TB diagnosis using this tool.

Area of Science:

  • Medical Imaging
  • Infectious Disease Diagnostics
  • Pediatric Radiology

Background:

  • Computer-aided detection (CAD) systems offer potential for enhancing tuberculosis (TB) detection on digital chest radiographs.
  • Limited data exists on the performance of CAD systems specifically in pediatric populations.

Purpose of the Study:

  • To evaluate the diagnostic accuracy of CAD4TBv7 in children under 15 years old with suspected pulmonary TB.
  • To assess CAD4TBv7 performance against a microbiological reference standard and through Bayesian latent class analysis.

Main Methods:

  • Retrospective diagnostic accuracy study of CAD4TBv7 on digital chest radiographs from Gambian children (<15 years).
  • Comparison against a microbiological reference standard (MRS) and Bayesian latent class analysis (LCA).
  • Performance metrics included area under the receiver operating characteristic curve (AUROC), sensitivity, and specificity.

Main Results:

  • CAD4TBv7 achieved an AUROC of 0.70 against MRS, with 19.0% sensitivity and 99.0% specificity.
  • Bayesian LCA yielded improved sensitivity estimates (42.7% to 50.3%) with high specificity (97.9% to 98.0%).
  • Results indicate high specificity but suboptimal sensitivity for pediatric TB detection.

Conclusions:

  • CAD4TBv7 demonstrates high specificity in children but requires significant optimization to improve sensitivity for TB detection.
  • Further research is needed to enhance CAD tools for accurate pediatric TB diagnosis.
Abstract

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