Performance of Human and Computer-aided Evaluation of Digital Chest Radiography for Community-based Screening of Asymptomatic Tuberculosis
Background: The World Health Organisation (WHO) recommends digital chest radiography (dCXR) with computer-aided detection (CAD) for tuberculosis (TB) screening of individuals >15 years of age. Methodology: Adults (≥18 years) were enrolled (March 2021-December 2022) in South Africa into a community-based Screening Cohort (household contacts) and a facility-based Triage Cohort (symptomatic clinic attendees). Microbiologically-confirmed pulmonary TB required positive sputum culture and/or Xpert Ultra. Asymptomatic TB was diagnosed in participants without TB symptoms. dCXR were read by blinded human readers and qXR CAD (0.5 threshold; Qure.AI, India). Results: dCXR from 1,353 participants (886 Screening Cohort; 467 Triage Cohort) were analysed. Microbiologically-confirmed TB occurred in 48 (5.4%) Screening Cohort [9 symptomatic (19%) and 39 asymptomatic (81%)]; and 116 (24.8%) Triage Cohort (all symptomatic) participants. dCXR sensitivity (human readers) for asymptomatic TB in the Screening Cohort was 56.4%, vs. 72.4% for symptomatic TB in the Triage Cohort (difference -16%; 95%CI -2.9 to -29.1); with specificities 94.1% and 81.2%, respectively. Corresponding qXR CAD sensitivities were 69.2% vs. 83.6% (difference -14.4%; 95%CI -26 to -2.8), with specificities 89.3% and 73.5%, respectively. The difference in dCXR sensitivity and specificity for asymptomatic TB between qXR CAD and human readers was 12.8% (95%CI -0.48 to 26.1) and -4.8% (95%CI -12.4 to 28.2), respectively. Conclusion: Sensitivity of community-based dCXR screening for microbiologically-confirmed asymptomatic TB among household contacts was lower than for facility-based triage of symptomatic TB, but approached 70% with CAD. Neither human reader nor qXR CAD evaluation met WHO targets for a TB screening test (90% sensitivity; 80% specificity). Research in context: transmission and hinder elimination efforts.
Authors
- A Leslie
- Stephanus T. Malherbe (ORCID: https://orcid.org/0000-0002-7563-8231)
- Fernanda Maruri (ORCID: https://orcid.org/0000-0001-9533-8239)
- Humphrey Mulenga (ORCID: https://orcid.org/0000-0002-2030-0751)
- Firdows Noor
- Kate Hadley
- Tahlia Perumal (ORCID: https://orcid.org/0000-0003-0679-5804)
- Ravindre Panchia (ORCID: https://orcid.org/0000-0002-3601-0839)
- Nicolette Tredoux
- Thomas Scriba
- Angelique K. Luabeya
- Justin Shenje
- Keertan Dheda
- Timothy R Sterling
- Tumelo Moloantoa
- Sarah Nyangu
- Gerhard Walzl
- Mark Hatherill (ORCID: https://orcid.org/0000-0003-3491-1809)
- Neil Martinson
- Khuthadzo Hlongwane
- Simon C Mendelsohn (ORCID: https://orcid.org/0000-0002-4054-2766)
- Michele Tameris
- Bernard Fourie
- Yuri F van der Heijden
- Kim Stanley
Institutions
- South African Medical Research Council (ZA)
- University of Cape Town (ZA)
- University of the Witwatersrand (ZA)
- Stellenbosch University (ZA)
- London School of Hygiene & Tropical Medicine (GB)
- South African Tuberculosis Vaccine Initiative (ZA)
- Aurum Institute (ZA)
- Africa Health Research Institute (ZA)
- Perinatal HIV Research Unit (ZA)
- University of Pretoria (ZA)
- Vanderbilt University Medical Center (US)
Publication Details
- Journal
- medRxiv
- Published
- 2026-04-30
- DOI
- https://doi.org/10.64898/2026.04.29.26351560
- Primary Topic
- COVID-19 diagnosis using AI
- Type
- article
- Field-Weighted Citation Impact
- 0.00