The DOI matches its Crossref record; 1 of the 1 references it cites were found in a registry or an index.
Automatic versus human reading of chest X-rays in the Zambia National Tuberculosis Prevalence Survey
Open accessAbstract
SETTING: Tuberculosis (TB) prevalence survey in Zambia between 2013 and 2014. OBJECTIVE: To compare the performance of automatic software (CAD4TB 5) in chest X-ray (CXR) reading with that of field (general practitioners) and central (radiologists) readers. DESIGN: A retrospective study comparing the performance of human and automatic reading was conducted. Two scenarios for central reading were evaluated: abnormalities not consistent with TB were considered to be 'normal' or 'abnormal'. Sputum culture was defined as the reference standard. Measures derived from receiver operating characteristic analysis were used to assess readers' performances. RESULTS: Of 46 099 participants, 23 838 cases included all survey information; of these, 106 cases were culture-confirmed TB-positive. The performance of CAD4TB 5 was similar to that of field and central readers. Although there were significant differences in specificity when compared with field readings (P = 0.002) and central readings considering the first scenario (P < 0.001), these differences were not substantial (93.2% vs. 92.6% and 98.4% vs. 99.6%, respectively).CONCLUSIONp: The performance of automatic CXR readings is comparable with that of human experts in a TB prevalence survey setting using culture as reference.
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Other studies have assumed that individuals who did not meet criteria for microbiological testing did not have tuberculosis.
“Other studies have assumed that individuals who did not meet the criteria for microbiological testing did not have tuberculosis [12, 28, 29].”
From Computer-aided detection thresholds for digital chest radiography interpretation in tuberculosis diagnostic algorithms. (2024) · cites this paper as [29]
Assuming those who did not undergo testing did not have tuberculosis will misclassify some individuals who have subclinical tuberculosis with normal chest radiograph.
“Assuming that those who did not undergo testing did not have tuberculosis is commonly done [12, 28, 29], but this will misclassify some individuals who have subclinical tuberculosis with normal chest radiograph, and addressing subclinical tuberculosis is essential if WHO incidence targets are to be met [40].”
From Computer-aided detection thresholds for digital chest radiography interpretation in tuberculosis diagnostic algorithms. (2024) · cites this paper as [29]
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Melendez, J., Sánchez, C. I., Philipsen, R. H. H. M., Maduskar, P., Dawson, R., Theron, G., Dheda, K., & van Ginneken, B. (2016) · Scientific Reports · doi:10.1038/srep25265 · Cited by 152
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Artificial Intelligence for healthcare in africa
Owoyemi, A., Owoyemi, J., Osiyemi, A., & Boyd, A. (2020) · Frontiers in Digital Health · doi:10.3389/fdgth.2020.00006 · Cited by 199
Fehr, J., Konigorski, S., Olivier, S., Gunda, R., Surujdeen, A., Gareta, D., Smit, T., Baisley, K., Moodley, S., Moosa, Y., et al. (2021) · npj Digital Medicine · doi:10.1038/s41746-021-00471-y · Cited by 74
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
“…underestimation of the specificity of CAD4TB. Assuming that those who did not undergo testing did not have tuberculosis is commonly done [12, 28, 29], but this will misclassify some individuals who have subclinical tuberculosis with normal chest radiograph, and addressing subclinical…”
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Metadata
- Authors
- Melendez, J., Philipsen, R. H. H. M., Chanda-Kapata, P., Sunkutu, V., Kapata, N., & van Ginneken, B.
- Affiliations
- Radboud University Medical Center (NL)
- Ministry of Health Headquarters, Lusaka, Zambia; Centre for Tropical Medicine and Travel Medicine, Department of Infectious Diseases, Academic Medical Centre, University of Amsterdam, Amsterdam, The Netherlands
- University Teaching Hospital (ZM)
- Ministry of Health (ZM)
- Corresponding author
- Melendez, J.
- Venue
- The International Journal of Tuberculosis and Lung Disease
- Published
- 2017-08-01
- Volume
- 21
- Issue
- 8
- Pages
- 880-886
- ISSN
- 1027-3719 (ISSN-L)
- OpenAlex
- W2744042391
- Cited by
- 36
- References
- 1
- Citations by year
- 2026: 4 · 2025: 2 · 2024: 8 · 2023: 2 · 2022: 6 · 2021: 6 · 2020: 3 · 2019: 2 · 2018: 3
- FWCI
- 1.68
- Type
- journal article
- Language
- en
- Open access
- green
- hdl.handle.net
- Licence
- other-oa
- Locations
- The International Journal of Tuberculosis and Lung Disease (publishedVersion)
- PubMed (publishedVersion)
- Data Archiving and Networked Services (DANS) (submittedVersion)
- Radboud Repository (Radboud University) (submittedVersion)
- Radboud Repository (Radboud University) (submittedVersion, other-oa)
- 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 › Pulmonary and Respiratory Medicine › Lung Cancer Diagnosis and Treatment
- Keywords
- tuberculosis, computer-aided detection, radiologist reading, diagnostic accuracy, Zambia
- MeSH terms
- Adult, Female, Humans, Male, Middle Aged, Surveys and Questionnaires, Retrospective Studies, Sensitivity and Specificity, Sputum, Radiography, Thoracic, Tuberculosis, Zambia, Prevalence, Young Adult
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