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Computer-assisted chest radiography reading for tuberculosis screening in people living with diabetes mellitus
Open accessAbstract
BACKGROUND: Diabetes mellitus is a significant risk factor for tuberculosis (TB). We evaluated the performance of computer-aided detection for tuberculosis (CAD4TB) in people living with diabetes mellitus (PLWD) in Indonesia. METHODS: PLWD underwent symptom screening and chest X-ray (CXR); sputum was examined in those with positive symptoms and/or CXR. Digital CXRs were scored using CAD4TB and analysed retrospectively using clinical and microbiological diagnosis as a reference. The area under the receiver operator curve (AUC) of CAD4TB scores was determined, and an optimal threshold score established. Agreement between CAD4TB and the radiologist's reading was determined. RESULTS: Among 346 included PLWD, seven (2.0%) had microbiologically confirmed and two (0.6%) had clinically diagnosed TB. The highest agreement of CAD4TB with radiologist reading was achieved using a threshold score of 70 (κ = 0.41, P < 0.001). The AUC for CAD4TB was 0.89 (95%CI 0.73-1.00). A threshold score of 65 for CAD4TB resulted in a sensitivity, specificity, positive predictive value and negative predictive value of respectively 88.9% (95%CI 51.8-99.7), 88.5% (95%CI 84.6-91.7), 17.0% (95%CI 7.6-30.8) and 99.6% (95%CI 98.2-100). With this threshold, 48 (13.9%) individuals needed microbiological examination and no microbiologically confirmed cases were missed. CONCLUSIONS: CAD4TB has potential as a triage tool for TB screening in PLWD, thereby significantly reducing the need for microbiological examination.
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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 [28]
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 [28]
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Jeon, C. Y., & Murray, M. B. (2008) · PLoS Medicine · doi:10.1371/journal.pmed.0050152 · Cited by 1,486
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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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
“…and 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
- Koesoemadinata, R. C., Kranzer, K., Livia, R., Susilawati, N., Annisa, J., Soetedjo, N. N. M., Ruslami, R., Philipsen, R., van Ginneken, B., Soetikno, R. D., van Crevel, R., Alisjahbana, B., & Hill, P. C.
- Affiliations
- Padjadjaran University (ID)
- London School of Hygiene & Tropical Medicine (GB)
- Infectious Disease Research Centre
- Dr. Hasan Sadikin General Hospital (ID)
- Radboud University Medical Center (NL)
- University of Otago (NZ)
- ORCID
- Venue
- The International Journal of Tuberculosis and Lung Disease
- Published
- 2018-09-01
- Volume
- 22
- Issue
- 9
- Pages
- 1088-1094
- ISSN
- 1027-3719 (ISSN-L)
- OpenAlex
- W2886671133
- Cited by
- 35
- References
- 2
- Citations by year
- 2026: 2 · 2025: 3 · 2024: 7 · 2023: 5 · 2022: 7 · 2021: 6 · 2020: 3 · 2019: 2
- FWCI
- 2.09
- 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)
- Radboud Repository (Radboud University) (submittedVersion)
- Radboud Repository (Radboud University) (submittedVersion, other-oa)
- Topics
- Health Sciences › Medicine › Infectious Diseases › Tuberculosis Research and Epidemiology
- Physical Sciences › Computer Science › Artificial Intelligence › AI in cancer detection
- Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › COVID-19 diagnosis using AI
- Keywords
- tuberculosis screening, diabetes mellitus, computer-aided detection, CAD4TB, chest radiography, triage tool, diagnostic accuracy, threshold scores, Indonesia
- MeSH terms
- Aged, Female, Humans, Image Processing, Computer-Assisted, Indonesia, Male, Mass Chest X-Ray, Middle Aged, Retrospective Studies, Sensitivity and Specificity, Sputum, Tuberculosis, Pulmonary, Area Under Curve, Diabetes Complications
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