An automated tuberculosis screening strategy combining X-ray-based computer-aided detection and clinical information

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dc.contributor.authorMelendez, Jaimeen_ZA
dc.contributor.authorSanchez, Claraen_ZA
dc.contributor.authorPhilipsen, Rick H. H. M.en_ZA
dc.contributor.authorMaduskar, Pragnyaen_ZA
dc.contributor.authorDawson, Rodneyen_ZA
dc.contributor.authorTheron, Granten_ZA
dc.contributor.authorDheda, Keertanen_ZA
dc.contributor.authorVan Ginneken, Bramen_ZA
dc.date.accessioned2017-10-23T09:05:45Z
dc.date.available2017-10-23T09:05:45Z
dc.date.issued2016
dc.descriptionCITATION: Melendez, J., et al. 2016. An automated tuberculosis screening strategy combining X-ray-based computer-aided detection and clinical information. Scientific Reports, 6:25265, doi:10.1038/srep25265.en_ZA
dc.descriptionThe original publication is available at http://www.nature.com/srepen_ZA
dc.description.abstractLack of human resources and radiological interpretation expertise impair tuberculosis (TB) screening programmes in TB-endemic countries. Computer-aided detection (CAD) constitutes a viable alternative for chest radiograph (CXR) reading. However, no automated techniques that exploit the additional clinical information typically available during screening exist. To address this issue and optimally exploit this information, a machine learning-based combination framework is introduced. We have evaluated this framework on a database containing 392 patient records from suspected TB subjects prospectively recruited in Cape Town, South Africa. Each record comprised a CAD score, automatically computed from a CXR, and 12 clinical features. Comparisons with strategies relying on either CAD scores or clinical information alone were performed. Our results indicate that the combination framework outperforms the individual strategies in terms of the area under the receiving operating characteristic curve (0.84 versus 0.78 and 0.72), specificity at 95% sensitivity (49% versus 24% and 31%) and negative predictive value (98% versus 95% and 96%). Thus, it is believed that combining CAD and clinical information to estimate the risk of active disease is a promising tool for TB screening.en_ZA
dc.description.urihttps://www.nature.com/articles/srep25265
dc.description.versionPublisher's versionen_ZA
dc.format.extent8 pages : illustrationsen_ZA
dc.identifier.citationMelendez, J., et al. 2016. An automated tuberculosis screening strategy combining X-ray-based computer-aided detection and clinical information. Scientific Reports, 6:25265, doi:10.1038/srep25265en_ZA
dc.identifier.issn2045-2322 (online)
dc.identifier.urihttp://hdl.handle.net/10019.1/102366
dc.language.isoen_ZAen_ZA
dc.publisherSpringer Natureen_ZA
dc.rights.holderAuthors retain copyrighten_ZA
dc.subjectTuberculosis screening strategyen_ZA
dc.subjectTuberculosis -- Diagnosis -- X-raysen_ZA
dc.subjectTuberculosis -- Diagnosis -- Computer-aided detectionen_ZA
dc.titleAn automated tuberculosis screening strategy combining X-ray-based computer-aided detection and clinical informationen_ZA
dc.typeArticleen_ZA
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