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Does imbalance in chest X-ray datasets produce biased deep learning approaches for COVID-19 screening?
BACKGROUND: The health crisis resulting from the global COVID-19 pandemic highlighted more than ever the need for rapid, reliable and safe methods of diagnosis and monitoring of respiratory diseases. To study pulmonary involvement in detail, one of the most common resources is the use of different l...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9046709/ https://www.ncbi.nlm.nih.gov/pubmed/35484483 http://dx.doi.org/10.1186/s12874-022-01578-w |
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author | Álvarez-Rodríguez, Lorena Moura, Joaquim de Novo, Jorge Ortega, Marcos |
author_facet | Álvarez-Rodríguez, Lorena Moura, Joaquim de Novo, Jorge Ortega, Marcos |
author_sort | Álvarez-Rodríguez, Lorena |
collection | PubMed |
description | BACKGROUND: The health crisis resulting from the global COVID-19 pandemic highlighted more than ever the need for rapid, reliable and safe methods of diagnosis and monitoring of respiratory diseases. To study pulmonary involvement in detail, one of the most common resources is the use of different lung imaging modalities (like chest radiography) to explore the possible affected areas. METHODS: The study of patient characteristics like sex and age in pathologies of this type is crucial for gaining knowledge of the disease and for avoiding biases due to the clear scarcity of data when developing representative systems. In this work, we performed an analysis of these factors in chest X-ray images to identify biases. Specifically, 11 imbalance scenarios were defined with female and male COVID-19 patients present in different proportions for the sex analysis, and 6 scenarios where only one specific age range was used for training for the age factor. In each study, 3 different approaches for automatic COVID-19 screening were used: Normal vs COVID-19, Pneumonia vs COVID-19 and Non-COVID-19 vs COVID-19. The study was validated using two public chest X-ray datasets, allowing a reliable analysis to support the clinical decision-making process. RESULTS: The results for the sex-related analysis indicate this factor slightly affects the system in the Normal VS COVID-19 and Pneumonia VS COVID-19 approaches, although the identified differences are not relevant enough to worsen considerably the system. Regarding the age-related analysis, this factor was observed to be influencing the system in a more consistent way than the sex factor, as it was present in all considered scenarios. However, this worsening does not represent a major factor, as it is not of great magnitude. CONCLUSIONS: Multiple studies have been conducted in other fields in order to determine if certain patient characteristics such as sex or age influenced these deep learning systems. However, to the best of our knowledge, this study has not been done for COVID-19 despite the urgency and lack of COVID-19 chest x-ray images. The presented results evidenced that the proposed methodology and tested approaches allow a robust and reliable analysis to support the clinical decision-making process in this pandemic scenario. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at (10.1186/s12874-022-01578-w). |
format | Online Article Text |
id | pubmed-9046709 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-90467092022-04-28 Does imbalance in chest X-ray datasets produce biased deep learning approaches for COVID-19 screening? Álvarez-Rodríguez, Lorena Moura, Joaquim de Novo, Jorge Ortega, Marcos BMC Med Res Methodol Research BACKGROUND: The health crisis resulting from the global COVID-19 pandemic highlighted more than ever the need for rapid, reliable and safe methods of diagnosis and monitoring of respiratory diseases. To study pulmonary involvement in detail, one of the most common resources is the use of different lung imaging modalities (like chest radiography) to explore the possible affected areas. METHODS: The study of patient characteristics like sex and age in pathologies of this type is crucial for gaining knowledge of the disease and for avoiding biases due to the clear scarcity of data when developing representative systems. In this work, we performed an analysis of these factors in chest X-ray images to identify biases. Specifically, 11 imbalance scenarios were defined with female and male COVID-19 patients present in different proportions for the sex analysis, and 6 scenarios where only one specific age range was used for training for the age factor. In each study, 3 different approaches for automatic COVID-19 screening were used: Normal vs COVID-19, Pneumonia vs COVID-19 and Non-COVID-19 vs COVID-19. The study was validated using two public chest X-ray datasets, allowing a reliable analysis to support the clinical decision-making process. RESULTS: The results for the sex-related analysis indicate this factor slightly affects the system in the Normal VS COVID-19 and Pneumonia VS COVID-19 approaches, although the identified differences are not relevant enough to worsen considerably the system. Regarding the age-related analysis, this factor was observed to be influencing the system in a more consistent way than the sex factor, as it was present in all considered scenarios. However, this worsening does not represent a major factor, as it is not of great magnitude. CONCLUSIONS: Multiple studies have been conducted in other fields in order to determine if certain patient characteristics such as sex or age influenced these deep learning systems. However, to the best of our knowledge, this study has not been done for COVID-19 despite the urgency and lack of COVID-19 chest x-ray images. The presented results evidenced that the proposed methodology and tested approaches allow a robust and reliable analysis to support the clinical decision-making process in this pandemic scenario. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at (10.1186/s12874-022-01578-w). BioMed Central 2022-04-28 /pmc/articles/PMC9046709/ /pubmed/35484483 http://dx.doi.org/10.1186/s12874-022-01578-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/) . The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/ (https://creativecommons.org/publicdomain/zero/1.0/) ) applies to the data made available in this article, unless otherwise stated in a credit line to the data. |
spellingShingle | Research Álvarez-Rodríguez, Lorena Moura, Joaquim de Novo, Jorge Ortega, Marcos Does imbalance in chest X-ray datasets produce biased deep learning approaches for COVID-19 screening? |
title | Does imbalance in chest X-ray datasets produce biased deep learning approaches for COVID-19 screening? |
title_full | Does imbalance in chest X-ray datasets produce biased deep learning approaches for COVID-19 screening? |
title_fullStr | Does imbalance in chest X-ray datasets produce biased deep learning approaches for COVID-19 screening? |
title_full_unstemmed | Does imbalance in chest X-ray datasets produce biased deep learning approaches for COVID-19 screening? |
title_short | Does imbalance in chest X-ray datasets produce biased deep learning approaches for COVID-19 screening? |
title_sort | does imbalance in chest x-ray datasets produce biased deep learning approaches for covid-19 screening? |
topic | Research |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9046709/ https://www.ncbi.nlm.nih.gov/pubmed/35484483 http://dx.doi.org/10.1186/s12874-022-01578-w |
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