Journal
INTERNATIONAL JOURNAL OF COMPUTER ASSISTED RADIOLOGY AND SURGERY
Volume 13, Issue 12, Pages 1915-1925Publisher
SPRINGER HEIDELBERG
DOI: 10.1007/s11548-018-1857-9
Keywords
Biomedical imaging; Machine learning; Computer-aided diagnosis; Tuberculosis; Drug resistance
Funding
- Intramural Research Program of the National Institutes of Health (NIH)
- National Library of Medicine (NLM)
- Lister Hill National Center for Biomedical Communications (LHNCBC)
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PurposeTuberculosis is a major global health threat claiming millions of lives each year. While the total number of tuberculosis cases has been decreasing over the last years, the rise of drug-resistant tuberculosis has reduced the chance of controlling the disease. The purpose is to implement a timely diagnosis of drug-resistant tuberculosis, which is essential to administering adequate treatment regimens and stopping the further transmission of drug-resistant tuberculosis.MethodsA main tool for diagnosing tuberculosis is the conventional chest X-ray. We are investigating the possibility of discriminating automatically between drug-resistant and drug-sensitive tuberculosis in chest X-rays by means of image analysis and machine learning methods.ResultsFor discriminating between drug-sensitive and drug-resistant tuberculosis, we achieve an area under the receiver operating characteristic curve (AUC) of up to 66%, using an artificial neural network in combination with a set of shape and texture features. We did not observe any significant difference in the results when including follow-up X-rays for each patient.ConclusionOur results suggest that a chest X-ray contains information about the likelihood of a drug-resistant tuberculosis infection, which can be exploited computationally. We therefore suggest to repeat the experiments of our pilot study on a larger set of chest X-rays.
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