Detecting drug-resistant tuberculosis in chest radiographs
| العنوان: | Detecting drug-resistant tuberculosis in chest radiographs |
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| المؤلفون: | Feng Yang, Octavio Juarez-Espinosa, George R. Thoma, Sameer Antani, Meng Ding, Alex Rosenthal, Darrell E. Hurt, Sema Candemir, Stefan Jaeger, Les R. Folio, Lewis Kim, Mahdieh Poostchi, Andrei Gabrielian |
| المصدر: | International Journal of Computer Assisted Radiology and Surgery |
| بيانات النشر: | Springer International Publishing, 2018. |
| سنة النشر: | 2018 |
| مصطلحات موضوعية: | Male, medicine.medical_specialty, Tuberculosis, Radiography, Biomedical Engineering, Health Informatics, Pilot Projects, 02 engineering and technology, Disease, Drug resistance, 030218 nuclear medicine & medical imaging, Diagnosis, Differential, Machine Learning, 03 medical and health sciences, 0302 clinical medicine, Biomedical imaging, Tuberculosis, Multidrug-Resistant, 0202 electrical engineering, electronic engineering, information engineering, Global health, Medicine, Humans, Radiology, Nuclear Medicine and imaging, Probability, Receiver operating characteristic, business.industry, Transmission (medicine), General Medicine, Computer-aided diagnosis, Middle Aged, medicine.disease, Computer Graphics and Computer-Aided Design, Computer Science Applications, ROC Curve, 020201 artificial intelligence & image processing, Surgery, Original Article, Female, Radiography, Thoracic, Computer Vision and Pattern Recognition, Radiology, Neural Networks, Computer, business, Tomography, X-Ray Computed |
| الوصف: | Purpose Tuberculosis 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. Methods A 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. Results For 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. Conclusion Our 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. |
| اللغة: | English |
| تدمد: | 1861-6429 1861-6410 |
| URL الوصول: | https://explore.openaire.eu/search/publication?articleId=doi_dedup___::f3782446431808c8f568c6efac1c773c http://europepmc.org/articles/PMC6223762 |
| حقوق: | OPEN |
| رقم الأكسشن: | edsair.doi.dedup.....f3782446431808c8f568c6efac1c773c |
| قاعدة البيانات: | OpenAIRE |
Find this article in full text from Springer Verlag. | تدمد: | 18616429 18616410 |
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