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Development of a multipotent diagnostic tool for chest X-rays by multi-object detection method

The computer-aided diagnosis (CAD) for chest X-rays was developed more than 50 years ago. However, there are still unmet needs for its versatile use in our medical fields. We planned this study to develop a multipotent CAD model suitable for general use including in primary care areas. We planned th...

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Autores principales: Kang, Minji, An, Tai Joon, Han, Deokjae, Seo, Wan, Cho, Kangwon, Kim, Shinbum, Myong, Jun-Pyo, Han, Sung Won
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9646869/
https://www.ncbi.nlm.nih.gov/pubmed/36352008
http://dx.doi.org/10.1038/s41598-022-21841-w
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author Kang, Minji
An, Tai Joon
Han, Deokjae
Seo, Wan
Cho, Kangwon
Kim, Shinbum
Myong, Jun-Pyo
Han, Sung Won
author_facet Kang, Minji
An, Tai Joon
Han, Deokjae
Seo, Wan
Cho, Kangwon
Kim, Shinbum
Myong, Jun-Pyo
Han, Sung Won
author_sort Kang, Minji
collection PubMed
description The computer-aided diagnosis (CAD) for chest X-rays was developed more than 50 years ago. However, there are still unmet needs for its versatile use in our medical fields. We planned this study to develop a multipotent CAD model suitable for general use including in primary care areas. We planned this study to solve the problem by using computed tomography (CT) scan with its one-to-one matched chest X-ray dataset. The data was extracted and preprocessed by pulmonology experts by using the bounding boxes to locate lesions of interest. For detecting multiple lesions, multi-object detection by faster R-CNN and by RetinaNet was adopted and compared. A total of twelve diagnostic labels were defined as the followings: pleural effusion, atelectasis, pulmonary nodule, cardiomegaly, consolidation, emphysema, pneumothorax, chemo-port, bronchial wall thickening, reticular opacity, pleural thickening, and bronchiectasis. The Faster R-CNN model showed higher overall sensitivity than RetinaNet, nevertheless the values of specificity were opposite. Some values such as cardiomegaly and chemo-port showed excellent sensitivity (100.0%, both). Others showed that the unique results such as bronchial wall thickening, reticular opacity, and pleural thickening can be described in the chest area. As far as we know, this is the first study to develop an object detection model for chest X-rays based on chest area defined by CT scans in one-to-one matched manner, preprocessed and conducted by a group of experts in pulmonology. Our model can be a potential tool for detecting the whole chest area with multiple diagnoses from a simple X-ray that is routinely taken in most clinics and hospitals on daily basis.
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spelling pubmed-96468692022-11-15 Development of a multipotent diagnostic tool for chest X-rays by multi-object detection method Kang, Minji An, Tai Joon Han, Deokjae Seo, Wan Cho, Kangwon Kim, Shinbum Myong, Jun-Pyo Han, Sung Won Sci Rep Article The computer-aided diagnosis (CAD) for chest X-rays was developed more than 50 years ago. However, there are still unmet needs for its versatile use in our medical fields. We planned this study to develop a multipotent CAD model suitable for general use including in primary care areas. We planned this study to solve the problem by using computed tomography (CT) scan with its one-to-one matched chest X-ray dataset. The data was extracted and preprocessed by pulmonology experts by using the bounding boxes to locate lesions of interest. For detecting multiple lesions, multi-object detection by faster R-CNN and by RetinaNet was adopted and compared. A total of twelve diagnostic labels were defined as the followings: pleural effusion, atelectasis, pulmonary nodule, cardiomegaly, consolidation, emphysema, pneumothorax, chemo-port, bronchial wall thickening, reticular opacity, pleural thickening, and bronchiectasis. The Faster R-CNN model showed higher overall sensitivity than RetinaNet, nevertheless the values of specificity were opposite. Some values such as cardiomegaly and chemo-port showed excellent sensitivity (100.0%, both). Others showed that the unique results such as bronchial wall thickening, reticular opacity, and pleural thickening can be described in the chest area. As far as we know, this is the first study to develop an object detection model for chest X-rays based on chest area defined by CT scans in one-to-one matched manner, preprocessed and conducted by a group of experts in pulmonology. Our model can be a potential tool for detecting the whole chest area with multiple diagnoses from a simple X-ray that is routinely taken in most clinics and hospitals on daily basis. Nature Publishing Group UK 2022-11-09 /pmc/articles/PMC9646869/ /pubmed/36352008 http://dx.doi.org/10.1038/s41598-022-21841-w Text en © The Author(s) 2022 https://creativecommons.org/licenses/by/4.0/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/ (https://creativecommons.org/licenses/by/4.0/) .
spellingShingle Article
Kang, Minji
An, Tai Joon
Han, Deokjae
Seo, Wan
Cho, Kangwon
Kim, Shinbum
Myong, Jun-Pyo
Han, Sung Won
Development of a multipotent diagnostic tool for chest X-rays by multi-object detection method
title Development of a multipotent diagnostic tool for chest X-rays by multi-object detection method
title_full Development of a multipotent diagnostic tool for chest X-rays by multi-object detection method
title_fullStr Development of a multipotent diagnostic tool for chest X-rays by multi-object detection method
title_full_unstemmed Development of a multipotent diagnostic tool for chest X-rays by multi-object detection method
title_short Development of a multipotent diagnostic tool for chest X-rays by multi-object detection method
title_sort development of a multipotent diagnostic tool for chest x-rays by multi-object detection method
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9646869/
https://www.ncbi.nlm.nih.gov/pubmed/36352008
http://dx.doi.org/10.1038/s41598-022-21841-w
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