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Diagnosis of common pulmonary diseases in children by X-ray images and deep learning
Acute lower respiratory infection is the leading cause of child death in developing countries. Current strategies to reduce this problem include early detection and appropriate treatment. Better diagnostic and therapeutic strategies are still needed in poor countries. Artificial-intelligence chest X...
Autores principales: | , , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group UK
2020
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7566516/ https://www.ncbi.nlm.nih.gov/pubmed/33060702 http://dx.doi.org/10.1038/s41598-020-73831-5 |
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author | Chen, Kai-Chi Yu, Hong-Ren Chen, Wei-Shiang Lin, Wei-Che Lee, Yi-Chen Chen, Hung-Hsun Jiang, Jyun-Hong Su, Ting-Yi Tsai, Chang-Ku Tsai, Ti-An Tsai, Chih-Min Lu, Henry Horng-Shing |
author_facet | Chen, Kai-Chi Yu, Hong-Ren Chen, Wei-Shiang Lin, Wei-Che Lee, Yi-Chen Chen, Hung-Hsun Jiang, Jyun-Hong Su, Ting-Yi Tsai, Chang-Ku Tsai, Ti-An Tsai, Chih-Min Lu, Henry Horng-Shing |
author_sort | Chen, Kai-Chi |
collection | PubMed |
description | Acute lower respiratory infection is the leading cause of child death in developing countries. Current strategies to reduce this problem include early detection and appropriate treatment. Better diagnostic and therapeutic strategies are still needed in poor countries. Artificial-intelligence chest X-ray scheme has the potential to become a screening tool for lower respiratory infection in child. Artificial-intelligence chest X-ray schemes for children are rare and limited to a single lung disease. We need a powerful system as a diagnostic tool for most common lung diseases in children. To address this, we present a computer-aided diagnostic scheme for the chest X-ray images of several common pulmonary diseases of children, including bronchiolitis/bronchitis, bronchopneumonia/interstitial pneumonitis, lobar pneumonia, and pneumothorax. The study consists of two main approaches: first, we trained a model based on YOLOv3 architecture for cropping the appropriate location of the lung field automatically. Second, we compared three different methods for multi-classification, included the one-versus-one scheme, the one-versus-all scheme and training a classifier model based on convolutional neural network. Our model demonstrated a good distinguishing ability for these common lung problems in children. Among the three methods, the one-versus-one scheme has the best performance. We could detect whether a chest X-ray image is abnormal with 92.47% accuracy and bronchiolitis/bronchitis, bronchopneumonia, lobar pneumonia, pneumothorax, or normal with 71.94%, 72.19%, 85.42%, 85.71%, and 80.00% accuracy, respectively. In conclusion, we provide a computer-aided diagnostic scheme by deep learning for common pulmonary diseases in children. This scheme is mostly useful as a screening for normal versus most of lower respiratory problems in children. It can also help review the chest X-ray images interpreted by clinicians and may remind possible negligence. This system can be a good diagnostic assistance under limited medical resources. |
format | Online Article Text |
id | pubmed-7566516 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | Nature Publishing Group UK |
record_format | MEDLINE/PubMed |
spelling | pubmed-75665162020-10-19 Diagnosis of common pulmonary diseases in children by X-ray images and deep learning Chen, Kai-Chi Yu, Hong-Ren Chen, Wei-Shiang Lin, Wei-Che Lee, Yi-Chen Chen, Hung-Hsun Jiang, Jyun-Hong Su, Ting-Yi Tsai, Chang-Ku Tsai, Ti-An Tsai, Chih-Min Lu, Henry Horng-Shing Sci Rep Article Acute lower respiratory infection is the leading cause of child death in developing countries. Current strategies to reduce this problem include early detection and appropriate treatment. Better diagnostic and therapeutic strategies are still needed in poor countries. Artificial-intelligence chest X-ray scheme has the potential to become a screening tool for lower respiratory infection in child. Artificial-intelligence chest X-ray schemes for children are rare and limited to a single lung disease. We need a powerful system as a diagnostic tool for most common lung diseases in children. To address this, we present a computer-aided diagnostic scheme for the chest X-ray images of several common pulmonary diseases of children, including bronchiolitis/bronchitis, bronchopneumonia/interstitial pneumonitis, lobar pneumonia, and pneumothorax. The study consists of two main approaches: first, we trained a model based on YOLOv3 architecture for cropping the appropriate location of the lung field automatically. Second, we compared three different methods for multi-classification, included the one-versus-one scheme, the one-versus-all scheme and training a classifier model based on convolutional neural network. Our model demonstrated a good distinguishing ability for these common lung problems in children. Among the three methods, the one-versus-one scheme has the best performance. We could detect whether a chest X-ray image is abnormal with 92.47% accuracy and bronchiolitis/bronchitis, bronchopneumonia, lobar pneumonia, pneumothorax, or normal with 71.94%, 72.19%, 85.42%, 85.71%, and 80.00% accuracy, respectively. In conclusion, we provide a computer-aided diagnostic scheme by deep learning for common pulmonary diseases in children. This scheme is mostly useful as a screening for normal versus most of lower respiratory problems in children. It can also help review the chest X-ray images interpreted by clinicians and may remind possible negligence. This system can be a good diagnostic assistance under limited medical resources. Nature Publishing Group UK 2020-10-15 /pmc/articles/PMC7566516/ /pubmed/33060702 http://dx.doi.org/10.1038/s41598-020-73831-5 Text en © The Author(s) 2020 Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. |
spellingShingle | Article Chen, Kai-Chi Yu, Hong-Ren Chen, Wei-Shiang Lin, Wei-Che Lee, Yi-Chen Chen, Hung-Hsun Jiang, Jyun-Hong Su, Ting-Yi Tsai, Chang-Ku Tsai, Ti-An Tsai, Chih-Min Lu, Henry Horng-Shing Diagnosis of common pulmonary diseases in children by X-ray images and deep learning |
title | Diagnosis of common pulmonary diseases in children by X-ray images and deep learning |
title_full | Diagnosis of common pulmonary diseases in children by X-ray images and deep learning |
title_fullStr | Diagnosis of common pulmonary diseases in children by X-ray images and deep learning |
title_full_unstemmed | Diagnosis of common pulmonary diseases in children by X-ray images and deep learning |
title_short | Diagnosis of common pulmonary diseases in children by X-ray images and deep learning |
title_sort | diagnosis of common pulmonary diseases in children by x-ray images and deep learning |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7566516/ https://www.ncbi.nlm.nih.gov/pubmed/33060702 http://dx.doi.org/10.1038/s41598-020-73831-5 |
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