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Qin, Z.Z., Ahmed, S., Sarker, M.S., Paul, K., Adel, A.S.S., Naheyan, T., et al. (2021) Tuberculosis Detection from Chest X-Rays for Triaging in a High Tuberculosis-Burden Setting: An Evaluation of Five Artificial Intelligence Algorithms. The Lancet Digital Health, 3, e543-e554.
https://doi.org/10.1016/s2589-7500(21)00116-3

has been cited by the following article:

• TITLE: Determining Threshold for Computer-Aided Detection (CAD) in Pre-Diagnostic Pulmonary TB Screening for Targeted Community TB Case Finding Using Portable Digital X-Ray in Nigeria

  AUTHORS: Bethrand Odume, Ojule Inumanye, Ordinioha Best, Aniwada Elias Chike

  KEYWORDS: Threshold Cut-Off, Computer-Aided Detection, Pre-Diagnostic TB Screening, Nigeria

  JOURNAL NAME: Journal of Tuberculosis Research, Vol.13 No.4, November 13, 2025

  ABSTRACT: Introduction: Computer-Aided Detection technologies for TB detection were considered by the WHO as part of an update to TB screening guidelines and recommendations. However, no universal guidelines exist for selecting a decision threshold. Most CAD software products do not come with a pre-set manufacturer-recommended threshold. Several studies suggested countries and setting specific threshold. This study seeks to calibrate CAD4TB to local settings and population groups in Nigeria. Methods: A retrospective review of data was done for all eligible participants identified from E. presumptive Register. Data was entered into a standardized CSV spreadsheet and uploaded to the online CAD for TB detection calibration tool developed by WHO for analysis of CAD data calibration studies. CAD score threshold cut-off point was based on Area Under Curve (AUC) in relation to other test modalities. Mean CAD scores were compared using Student T test at p Result: The Mean CAD4 score was 43.13 (Range 0 to 99.3). Those located at health facility have highest mean 55.05 followed by those in slums 49.53 with p Conclusion: Findings provide valuable insights into calibration of CAD4TB for the entire population. Integrating CAD4TB into routine TB detection programs can help bridge gaps in TB case detection, leading to better health outcomes globally.