ArticlePLOS global public health2026
Computer-aided detection pragmatic threshold calibration in TB active case finding: A mixed-methods study in Tondo, Manila.
Article in PLOS global public health, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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Who cites it
1 citing paper in PubMed.
- Implementing computer‑aided detection for TB active case finding: A qualitative study of feasibility and acceptability in Tondo, Manila.PLOS global public health · 2026Article
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Authors and funding
13 authors.
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Abstract
We developed a pragmatic approach to calibrating computer-aided detection (CAD) thresholds in a tuberculosis (TB) active case finding (ACF) project in the Philippines, assessing its feasibility and acceptability in a high-burden urban setting. CAD4TB v.7 (Delft Imaging, Netherlands) software was integrated into ACF activities targeting individuals aged ≥15 years in Tondo, Manila, from November 2022. Referrals for sputum collection and Xpert testing were based on CAD scores above a threshold. We used a mixed-methods approach: 1) retrospective analysis of pre-CAD data (May-November 2022) to determine the initial threshold; 2) prospective monitoring (November 2022-October 2023) to adjust the threshold; 3) semi-structured interviews with healthcare workers and stakeholders (January-April 2023). Threshold selection combined quantitative data, user experiences, and operational observations. The initial CAD threshold of 25 matched the 35% referral rate of onsite radiologists. After two weeks, referral rose to 37.5%, prompting an increase to 28. Eight months later, referral was 32.6% with a 4.9% screening yield (Xpert positive among screened individuals). Following data and stakeholders' input, the threshold was further increased to 32, after which referral dropped below 25% and the yield was 4.2%. Overall, 14 739 individuals were screened from October 2022 to October 2023, with a total screening yield of 4.3%, resulting in 633 TB cases. CAD threshold calibration, informed by referral rate targets, screening yield, and operational constraints can improve ACF efficiency and user confidence. Adjustments informed by real-time monitoring and stakeholder feedback helped balance sensitivity and operational feasibility in a mobile, underserved population. This framework supports tailoring CAD deployment to local epidemiology, software versioning, and system capacity. It is applicable to other high-burden settings, although further validation across diverse populations and assessment of subgroup-specific thresholds are warranted.
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Registered trials
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