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Detecting drug-resistant tuberculosis in chest radiographs
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 t...
Autores principales: | , , , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Springer International Publishing
2018
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6223762/ https://www.ncbi.nlm.nih.gov/pubmed/30284153 http://dx.doi.org/10.1007/s11548-018-1857-9 |
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author | Jaeger, Stefan Juarez-Espinosa, Octavio H. Candemir, Sema Poostchi, Mahdieh Yang, Feng Kim, Lewis Ding, Meng Folio, Les R. Antani, Sameer Gabrielian, Andrei Hurt, Darrell Rosenthal, Alex Thoma, George |
author_facet | Jaeger, Stefan Juarez-Espinosa, Octavio H. Candemir, Sema Poostchi, Mahdieh Yang, Feng Kim, Lewis Ding, Meng Folio, Les R. Antani, Sameer Gabrielian, Andrei Hurt, Darrell Rosenthal, Alex Thoma, George |
author_sort | Jaeger, Stefan |
collection | PubMed |
description | 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. |
format | Online Article Text |
id | pubmed-6223762 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2018 |
publisher | Springer International Publishing |
record_format | MEDLINE/PubMed |
spelling | pubmed-62237622018-11-18 Detecting drug-resistant tuberculosis in chest radiographs Jaeger, Stefan Juarez-Espinosa, Octavio H. Candemir, Sema Poostchi, Mahdieh Yang, Feng Kim, Lewis Ding, Meng Folio, Les R. Antani, Sameer Gabrielian, Andrei Hurt, Darrell Rosenthal, Alex Thoma, George Int J Comput Assist Radiol Surg Original Article 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. Springer International Publishing 2018-10-03 2018 /pmc/articles/PMC6223762/ /pubmed/30284153 http://dx.doi.org/10.1007/s11548-018-1857-9 Text en © The Author(s) 2018 Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. |
spellingShingle | Original Article Jaeger, Stefan Juarez-Espinosa, Octavio H. Candemir, Sema Poostchi, Mahdieh Yang, Feng Kim, Lewis Ding, Meng Folio, Les R. Antani, Sameer Gabrielian, Andrei Hurt, Darrell Rosenthal, Alex Thoma, George Detecting drug-resistant tuberculosis in chest radiographs |
title | Detecting drug-resistant tuberculosis in chest radiographs |
title_full | Detecting drug-resistant tuberculosis in chest radiographs |
title_fullStr | Detecting drug-resistant tuberculosis in chest radiographs |
title_full_unstemmed | Detecting drug-resistant tuberculosis in chest radiographs |
title_short | Detecting drug-resistant tuberculosis in chest radiographs |
title_sort | detecting drug-resistant tuberculosis in chest radiographs |
topic | Original Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6223762/ https://www.ncbi.nlm.nih.gov/pubmed/30284153 http://dx.doi.org/10.1007/s11548-018-1857-9 |
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