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Computer aided detection of tuberculosis on chest radiographs: An evaluation of the CAD4TB v6 system
Open accessAbstract
There is a growing interest in the automated analysis of chest X-Ray (CXR) as a sensitive and inexpensive means of screening susceptible populations for pulmonary tuberculosis. In this work we evaluate the latest version of CAD4TB, a commercial software platform designed for this purpose. Version 6 of CAD4TB was released in 2018 and is here tested on a fully independent dataset of 5565 CXR images with GeneXpert (Xpert) sputum test results available (854 Xpert positive subjects). A subset of 500 subjects (50% Xpert positive) was reviewed and annotated by 5 expert observers independently to obtain a radiological reference standard. The latest version of CAD4TB is found to outperform all previous versions in terms of area under receiver operating curve (ROC) with respect to both Xpert and radiological reference standards. Improvements with respect to Xpert are most apparent at high sensitivity levels with a specificity of 76% obtained at a fixed 90% sensitivity. When compared with the radiological reference standard, CAD4TB v6 also outperformed previous versions by a considerable margin and achieved 98% specificity at the 90% sensitivity setting. No substantial difference was found between the performance of CAD4TB v6 and any of the various expert observers against the Xpert reference standard. A cost and efficiency analysis on this dataset demonstrates that in a standard clinical situation, operating at 90% sensitivity, users of CAD4TB v6 can process 132 subjects per day at an average cost per screen of $ 5.95 per subject, while users of version 3 process only 85 subjects per day at a cost of $ 8.38 per subject. At all tested operating points version 6 is shown to be more efficient and cost effective than any other version.
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CAD at varied thresholds achieves higher sensitivity than human readers.
“As in other studies, we have demonstrated that CAD at varied thresholds achieves higher sensitivity than human readers [6,8,14].”
From Accuracy of computer-aided chest X-ray in community-based tuberculosis screening: Lessons from the 2016 Kenya National Tuberculosis Prevalence Survey (2022) · cites this paper as [6]
The Results (CAD4TB performance) compare CAD4TB v6 across operating points with five human readers and report a significant advantage over one non-physician reader at high sensitivities. They describe its performance as very similar to expert readers, not generally more sensitive than human readers at varied thresholds. (LLM, conf=0.86)
Experimental — accuracy not yet measuredThe majority of studies reporting on CAD threshold determination only included individuals who met criteria for microbiological testing.
“The majority of studies reporting on CAD threshold determination only included individuals who met some criteria for microbiological testing, such as having symptoms and/or abnormal chest radiographs [8, 16–27].”
From Computer-aided detection thresholds for digital chest radiography interpretation in tuberculosis diagnostic algorithms. (2024) · cites this paper as [20]
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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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Research works printed but not linked: 14
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How does artificial intelligence in radiology improve efficiency and health outcomes?
van Leeuwen, K. G., de Rooij, M., Schalekamp, S., van Ginneken, B., & Rutten, M. J. C. M. (2022) · Pediatric Radiology · doi:10.1007/s00247-021-05114-8 · Cited by 236
Self-citationFehr, 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
Mungai, B., Ong‘angò, J., Ku, C. C., Henrion, M. Y. R., Morton, B., Joekes, E., Onyango, E., Kiplimo, R., Kirathe, D., Masini, E., et al. (2022) · PLOS Global Public Health · doi:10.1371/journal.pgph.0001272 · Cited by 24
“CAD software that provides a probabilistic score for TB offers a potential solution to these limitations [6,8,15].”
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
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Metadata
- Authors
- Murphy, K., Habib, S. S., Zaidi, S. M. A., Khowaja, S., Khan, A., Melendez, J., Scholten, E. T., Amad, F., Schalekamp, S., Verhagen, M., Philipsen, R. H. H. M., Meijers, A., & van Ginneken, B.
- Affiliations
- Radboud University Medical Center (NL)
- Indus Hospital (PK)
- Corresponding author
- Murphy, Keelin
- ORCID
- Venue
- Scientific Reports
- Published
- 2020-03-26
- Volume
- 10
- Issue
- 1
- Pages
- 5492-5492
- ISSN
- 2045-2322 (ISSN-L)
- PMC
- PMC7099074
- OpenAlex
- W2921856303
- Cited by
- 122
- References
- 1
- Citations by year
- 2026: 18 · 2025: 13 · 2024: 25 · 2023: 18 · 2022: 18 · 2021: 23 · 2020: 5
- FWCI
- 7.79
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Locations
- Scientific Reports (publishedVersion, cc-by)
- arXiv (Cornell University) (publishedVersion)
- Europe PMC (PubMed Central) (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- Radboud Repository (Radboud University) (submittedVersion)
- Radboud Repository (Radboud University) (submittedVersion, other-oa)
- arXiv (Cornell University)
- Topics
- Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › COVID-19 diagnosis using AI
- Health Sciences › Medicine › Infectious Diseases › Tuberculosis Research and Epidemiology
- Physical Sciences › Computer Science › Artificial Intelligence › AI in cancer detection
- Keywords
- computer-aided detection, pulmonary tuberculosis, chest radiograph, CAD4TB, automated screening, diagnostic accuracy, cost-effectiveness, tuberculosis screening
- MeSH terms
- Adult, Expert Testimony, Female, Humans, Male, Middle Aged, Pakistan, Radiographic Image Interpretation, Computer-Assisted, Sensitivity and Specificity, Software, Radiography, Thoracic, Tuberculosis, Pulmonary, Observer Variation, Databases, Factual, Young Adult
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