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. 2024 Nov 7;64(5):2400811.
doi: 10.1183/13993003.00811-2024. Print 2024 Nov.

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

Victory Fabian Edem  1   2   3 , Esin Nkereuwem  1   4   3 , Schadrac C Agbla  5   6 , Sheila A Owusu  1   4   7 , Abdou K Sillah  1 , Binta Saidy  1 , Musa B Jallow  8 , Audrey G Forson  9 , Uzochukwu Egere  10 , Beate Kampmann  1   4   11 , Toyin Togun  12   4   13
Affiliations

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

Victory Fabian Edem et al. Eur Respir J. .

Abstract

Background: Computer-aided detection (CAD) systems hold promise for improving tuberculosis (TB) detection on digital chest radiographs. However, data on their performance in exclusively paediatric populations are scarce.

Methods: We conducted a retrospective diagnostic accuracy study evaluating the performance of CAD4TBv7 (Computer-Aided Detection for Tuberculosis version 7) using digital chest radiographs from well-characterised cohorts of Gambian children aged <15 years with presumed pulmonary TB. The children were consecutively recruited between 2012 and 2022. We measured CAD4TBv7 performance against a microbiological reference standard (MRS) of confirmed TB, and also performed Bayesian latent class analysis (LCA) to address the inherent limitations of the MRS in children. Diagnostic performance was assessed using the area under the receiver operating characteristic curve (AUROC) and point estimates of sensitivity and specificity.

Results: A total of 724 children were included in the analysis, with confirmed TB in 58 (8%), unconfirmed TB in 145 (20%) and unlikely TB in 521 (72%). Using the MRS, CAD4TBv7 showed an AUROC of 0.70 (95% CI 0.60-0.79), and demonstrated sensitivity and specificity of 19.0% (95% CI 11-31%) and 99.0% (95% CI 98.0-100.0%), respectively. Applying Bayesian LCA with the assumption of conditional independence between tests, sensitivity and specificity estimates for CAD4TBv7 were 42.7% (95% CrI 29.2-57.5%) and 97.9% (95% CrI 96.6-98.8%), respectively. When allowing for conditional dependence between culture and Xpert assay, CAD4TBv7 demonstrated a sensitivity of 50.3% (95% CrI 32.9-70.0%) and specificity of 98.0% (95% CrI 96.7-98.9%).

Conclusion: Although CAD4TBv7 demonstrated high specificity, its suboptimal sensitivity underscores the crucial need for optimisation of CAD4TBv7 for detecting TB in children.

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Conflict of interest statement

Conflict of interest: The authors have no potential conflicts of interest to disclose.

Figures

None
Main findings of the study. Although CAD4TBv7 (Computer-Aided Detection for Tuberculosis version 7) demonstrated high specificity, its suboptimal sensitivity underscores the crucial need for optimisation for detection of tuberculosis (TB) in children. MGIT: mycobacteria growth indicator tube; MRS: microbiological reference standard; CI: confidence interval; CrI: credible interval.
FIGURE 1
FIGURE 1
STARD diagram reporting the flow of participants in the study. TB: tuberculosis; MRS: microbiological reference standard.
FIGURE 2
FIGURE 2
a) Violin plots of CAD4TBv7 scores stratified by diagnostic categories. The manufacturer-recommended threshold score (≥60) is indicated by the dashed red line. b) Scatter plots of CAD4TBv7 scores by diagnostic categories, stratified by HIV status. TB: tuberculosis.
FIGURE 3
FIGURE 3
Area under the receiver operating characteristic curve (AUROC) with 95% confidence interval of CAD4TBv7 using the manufacturer-recommended threshold and verified against the microbiological reference standard: a) overall, b) by age category and c) by source (contact traced versus referred).
FIGURE 4
FIGURE 4
Sensitivity and specificity of CAD4TBv7 using the manufacturer-recommended threshold and verified against the microbiological reference standard.
See this image and copyright information in PMC

Comment in

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