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CAD4TB software updates: different triaging thresholds require caution by users and regulation by authorities
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different triaging thresholds require caution by users and regulation by authorities
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In a community-based study in South Africa restricted to participants with sputum test results, a threshold of 20 was estimated to most closely match radiologist sensitivity of 81%, with specificity 57%.
“In the second, a community-based study in South Africa restricted to participants with sputum test results, a threshold of 20 was estimated (to most closely match radiologist sensitivity of 81%, with specificity 57%) [42].”
From Computer-aided detection thresholds for digital chest radiography interpretation in tuberculosis diagnostic algorithms. (2024) · cites this paper as [42]
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Research works printed but not linked: 12
- [1]World Health Organization WHO consolidated guidelines on tuberculosis. Module 2: Screening Systematic screening for tuberculosis disease Geneva, Switzerland WHO 2021 33822560
- [2]Fehr J Computer-aided interpretation of chest radiography reveals the spectrum of tuberculosis in rural South Africa Npj Digit Med 2021 4 1 20 34215836 PMC8253848
- [3]Zaidi SMA Evaluation of the diagnostic accuracy of Computer-Aided Detection of tuberculosis on chest radiography among private sector patients in Pakistan Sci Rep 2018 8 1 1 9 30120345 PMC6098114
- [4]Koesoemadinata RC Computer-assisted chest radiography reading for tuberculosis screening in people living with diabetes mellitus Int J Tuberc Lung Dis 2018 22 9 1088 1094 30092877
- [5]Qin ZZ Tuberculosis detection from chest x-rays for triaging in a high tuberculosis-burden setting: an evaluation of five artificial intelligence algorithms Lancet Digit Health 2021 3 9 e543 554 34446265
- [6]Khan FA Articles Chest x-ray analysis with deep learning-based software as a triage test for pulmonary tuberculosis: a prospective study of diagnostic accuracy for culture-confirmed disease Lancet Digit Health 2020 2 11 e573 581 33328086
- [7]Tavaziva G Chest X-ray analysis with deep learning-based software as a triage test for pulmonary tuberculosis: an individual patient data meta-analysis of diagnostic accuracy Clin Infect Dis 2022 74 8 1390 1400 34286831 PMC9049274
- [8]Qin ZZ Comparing different versions of computer-aided detection products when reading chest X-rays for tuberculosis PLoS Digit Health 2022 1 6 e0000067 PMC9931298 36812562
- [9]Murphy K Computer aided detection of tuberculosis on chest radiographs: an evaluation of the CAD4TB v6 system Sci Rep 2020 10 1 1 11 32218458 PMC7099074
- [10]Wong EB Convergence of infectious and non-communicable disease epidemics in rural South Africa: a cross-sectional, population-based multimorbidity study Lancet Glob Health 2021 9 7 e967 976 34143995 PMC8220132
- [13]Qin ZZ Using artificial intelligence to read chest radiographs for tuberculosis detection: a multi-site evaluation of the diagnostic accuracy of three deep learning systems Sci Rep 2019 9 15000 31628424 PMC6802077
- [15]Qin ZZ A new resource on artificial intelligence powered computer automated detection software products for tuberculosis programmes and implementers Tuberculosis 2021 127 102049 33440315
Cited by
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Vanobberghen, F., Keter, A. K., Jacobs, B. K. M., Glass, T. R., Lynen, L., Law, I., Murphy, K., van Ginneken, B., Ayakaka, I., van Heerden, A., et al. (2024) · ERJ Open Research · doi:10.1183/23120541.00508-2023 · Cited by 13
“[42].”
Metadata
- Authors
- Fehr, J., Gunda, R., Siedner, M. J., Hanekom, W., Ndung´u, T., Grant, A., Lippert, C., & Wong, E. B.
- Affiliations
- Hasso Plattner Institute (DE)
- Africa Health Research Institute (ZA)
- Harvard University (US)
- Africa Health Research Institute, KwaZulu-Natal, South Africa, Division of Infection and Immunity, University College London, London, UK, HIV Pathogenesis Programme, The Doris Duke Medical Research Institute, University of KwaZulu-Natal, Durban, South Africa, Ragon Institute of MGH, MIT and Harvard University, Cambridge, MA, USA
- London School of Hygiene & Tropical Medicine (GB)
- University of Alabama at Birmingham (US)
- Corresponding author
- Wong, E. B.
- Venue
- The International Journal of Tuberculosis and Lung Disease
- Published
- 2023-02-01
- Volume
- 27
- Issue
- 2
- Pages
- 157-160
- ISSN
- 1027-3719 (ISSN-L)
- PMC
- PMC9904401
- OpenAlex
- W4317888682
- Cited by
- 19
- References
- 0
- Citations by year
- 2026: 5 · 2025: 6 · 2024: 5 · 2023: 3
- FWCI
- 3.93
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- cc-by
- Locations
- The International Journal of Tuberculosis and Lung Disease (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- UCL Discovery (University College London) (submittedVersion, cc-by)
- LSHTM Research Online (London School of Hygiene and Tropical Medicine) (submittedVersion, other-oa)
- Funders
- Massachusetts General Hospital
- Harvard University
- Wellcome Trust (201433, 201433/Z/16/A)
- University College London
- Universität Potsdam
- Inyuvesi Yakwazulu-Natali
- Topics
- Health Sciences › Medicine › Infectious Diseases › Tuberculosis Research and Epidemiology
- Health Sciences › Medicine › Epidemiology › Data-Driven Disease Surveillance
- Health Sciences › Medicine › Public Health, Environmental and Occupational Health › Healthcare Regulation
- Keywords
- CAD4TB, software updates, regulatory oversight
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