The DOI matches its Crossref record; 14 of the 14 references it cites were found in a registry or an index.
Computer-aided reading of tuberculosis chest radiography: moving the research agenda forward to inform policy
Open accessAbstract
Key gaps limit the evidence base for computer-aided reading of TB on CXR.We describe a research agenda to fill them http://ow.
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Some CAD developers recommend a universal threshold score, while others recommend setting-specific thresholds due to variation in CAD abnormality scores between software and populations.
“Some developers recommend a threshold score for universal use, above which an individual would be classified as having abnormalities suggestive of tuberculosis [6, 7]. Other developers, such as CAD4TB (Delft Imaging Systems, Netherlands), recommend scores tailored to each setting due to variation in CAD abnormality scores between different software and populations [5, 6, 8–13]”
From Computer-aided detection thresholds for digital chest radiography interpretation in tuberculosis diagnostic algorithms. (2024) · cites this paper as [13]
The ‘Current landscape of radiography and computer-aided detection’ section says CAD4TB’s developer does not recommend a fixed threshold and advises users to choose one using data from the intended population. The ‘Research questions to be addressed for policy guidance’ section says thresholds may differ across use cases. Neither passage establishes that other developers recommend a universal threshold or that differences between software explain score variation; page numbers were not supplied. (LLM, conf=0.86)
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Cites
STARD 2015: an updated list of essential items for reporting diagnostic accuracy studies
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“Reporting should follow guidelines for reporting diagnostic accuracy studies [17].”
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Towards tuberculosis elimination: an action framework for low-incidence countries
Lönnroth, K., Migliori, G. B., Abubakar, I. I., D’Ambrosio, L., de Vries, G., Diel, R., Douglas, P. J., Falzon, D., Gaudreau, M.-A., Goletti, D., et al. (2015) · European Respiratory Journal · doi:10.1183/09031936.00214014 · Cited by 780
Self-citationDigital radiography. A comparison with modern conventional imaging
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Understanding the incremental value of novel diagnostic tests for tuberculosis
Arinaminpathy, N., & Dowdy, D. W. (2015) · Nature · doi:10.1038/nature16045 · Cited by 118
Computer-aided detection of pulmonary tuberculosis on digital chest radiographs: a systematic review
Pande, T., Cohen, C., Pai, M., & Khan, F. A. (2016) · The International Journal of Tuberculosis and Lung Disease · doi:10.5588/ijtld.15.0926 · Cited by 115
Self-citationBreuninger, M., van Ginneken, B., Philipsen, R. H. H. M., Mhimbira, F., Hella, J. J., Lwilla, F., van den Hombergh, J., Ross, A., Jugheli, L., Wagner, D., et al. (2014) · PLoS ONE · doi:10.1371/journal.pone.0106381 · Cited by 101
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Self-citationUse of chest radiography in the 22 highest tuberculosis burden countries
Pande, T., Pai, M., Khan, F. A., & Denkinger, C. M. (2015) · European Respiratory Journal · doi:10.1183/13993003.01064-2015 · Cited by 62
Self-citationHoog, A. H. V., Cobelens, F. G. J., Vassall, A., van Kampen, S. C., Dorman, S. E., Alland, D., & Ellner, J. J. (2013) · PLoS ONE · doi:10.1371/journal.pone.0082786 · Cited by 58
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Self-citationCan tuberculosis patients in resource-constrained settings afford chest radiography?
Pedrazzoli, D., Lalli, M., Boccia, D., et al. (2016) · European Respiratory Journal · doi:10.1183/13993003.01877-2016 · Cited by 46
Murray, M. P., Cattamanchi, A., Denkinger, C. M., Hoog, A. H. V., Pai, M., & Dowdy, D. W. (2016) · BMJ Global Health · doi:10.1136/bmjgh-2016-000064 · Cited by 14
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Research works printed but not linked: 1
- Rabinovitch, 2013, Radiology of tuberculosis: case studies for the GP, GP Clinics, 4, 21
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Qin, Z. Z., Sander, M. S., Rai, B., Titahong, C. N., Sudrungrot, S., Laah, S. N., Adhikari, L. M., Carter, E. J., Puri, L., Codlin, A. J., et al. (2019) · Scientific Reports · doi:10.1038/s41598-019-51503-3 · Cited by 289
MacPherson, P., Webb, E. L., Kamchedzera, W., Joekes, E., Mjoli, G., Lalloo, D. G., Divala, T. H., Choko, A. T., Burke, R. M., Maheswaran, H., et al. (2021) · PLOS Medicine · doi:10.1371/journal.pmed.1003752 · Cited by 69
Self-citationGeric, C., Qin, Z. Z., Denkinger, C. M., Kik, S. V., Marais, B., Anjos, A., David, P.-M., Ahmad Khan, F., & Trajman, A. (2023) · The International Journal of Tuberculosis and Lung Disease · doi:10.5588/ijtld.22.0687 · Cited by 61
Self-citationVanobberghen, 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
“…CAD4TB (Delft Imaging Systems, Netherlands), recommend scores tailored to each setting due to variation in CAD abnormality scores between different software and populations [5, 6, 8 – 13], yet context-specific determination is often not done due to logistical and financial constraints [8, 14, 15].”
Background
Metadata
- Authors
- Ahmad Khan, F., Pande, T., Tessema, B., Song, R., Benedetti, A., Pai, M., Lönnroth, K., & Denkinger, C. M.
- Affiliations
- McGill University (CA)
- FIND (CH)
- McGill University Health Centre (CA)
- Karolinska Institutet (SE)
- ORCID
- Venue
- European Respiratory Journal
- Publisher
- European Respiratory Society (ERS)
- Published
- 2017-07
- Volume
- 50
- Issue
- 1
- Pages
- 1700953
- ISSN
- 0903-1936 (ISSN-L)
- 1399-3003
- OpenAlex
- W2735102775
- Cited by
- 59
- References
- 14
- Citations by year
- 2026: 3 · 2025: 4 · 2024: 9 · 2023: 11 · 2022: 10 · 2021: 7 · 2020: 6 · 2019: 6 · 2018: 3
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- European Respiratory Journal (publishedVersion)
- PubMed (publishedVersion)
- Funders
- United States Agency for International Development
- World Health Organization
- National Institutes of Health
- National Institute of Child Health and Human Development
- Eunice Kennedy Shriver National Institute of Child Health and Human Development (K23HD072802)
- Topics
- Health Sciences › Medicine › Infectious Diseases › Tuberculosis Research and Epidemiology
- Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › COVID-19 diagnosis using AI
- Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Radiomics and Machine Learning in Medical Imaging
- Keywords
- tuberculosis, chest radiography, computer-aided diagnosis, research agenda, evidence base, health policy, tuberculosis screening
- MeSH terms
- Algorithms, Diagnosis, Computer-Assisted, Health Policy, Humans, Software, Radiography, Thoracic, Tuberculosis, Global Health, World Health Organization, Pulmonary Medicine, Evidence-Based Medicine
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