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Using Deep Neural Networks for Predicting Age and Sex in Healthy Adult Chest Radiographs
Background: The performance of chest radiography-based age and sex prediction has not been well validated. We used a deep learning model to predict the age and sex of healthy adults based on chest radiographs (CXRs). Methods: In this retrospective study, 66,643 CXRs of 47,060 healthy adults were use...
Autores principales: | , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8509558/ https://www.ncbi.nlm.nih.gov/pubmed/34640449 http://dx.doi.org/10.3390/jcm10194431 |
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author | Yang, Chung-Yi Pan, Yi-Ju Chou, Yen Yang, Chia-Jung Kao, Ching-Chung Huang, Kuan-Chieh Chang, Jing-Shan Chen, Hung-Chieh Kuo, Kuei-Hong |
author_facet | Yang, Chung-Yi Pan, Yi-Ju Chou, Yen Yang, Chia-Jung Kao, Ching-Chung Huang, Kuan-Chieh Chang, Jing-Shan Chen, Hung-Chieh Kuo, Kuei-Hong |
author_sort | Yang, Chung-Yi |
collection | PubMed |
description | Background: The performance of chest radiography-based age and sex prediction has not been well validated. We used a deep learning model to predict the age and sex of healthy adults based on chest radiographs (CXRs). Methods: In this retrospective study, 66,643 CXRs of 47,060 healthy adults were used for model training and testing. In total, 47,060 individuals (mean age ± standard deviation, 38.7 ± 11.9 years; 22,144 males) were included. By using chronological ages as references, mean absolute error (MAE), root mean square error (RMSE), and Pearson’s correlation coefficient were used to assess the model performance. Summarized class activation maps were used to highlight the activated anatomical regions. The area under the curve (AUC) was used to examine the validity for sex prediction. Results: When model predictions were compared with the chronological ages, the MAE was 2.1 years, RMSE was 2.8 years, and Pearson’s correlation coefficient was 0.97 (p < 0.001). Cervical, thoracic spines, first ribs, aortic arch, heart, rib cage, and soft tissue of thorax and flank seemed to be the most crucial activated regions in the age prediction model. The sex prediction model demonstrated an AUC of >0.99. Conclusion: Deep learning can accurately estimate age and sex based on CXRs. |
format | Online Article Text |
id | pubmed-8509558 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-85095582021-10-13 Using Deep Neural Networks for Predicting Age and Sex in Healthy Adult Chest Radiographs Yang, Chung-Yi Pan, Yi-Ju Chou, Yen Yang, Chia-Jung Kao, Ching-Chung Huang, Kuan-Chieh Chang, Jing-Shan Chen, Hung-Chieh Kuo, Kuei-Hong J Clin Med Article Background: The performance of chest radiography-based age and sex prediction has not been well validated. We used a deep learning model to predict the age and sex of healthy adults based on chest radiographs (CXRs). Methods: In this retrospective study, 66,643 CXRs of 47,060 healthy adults were used for model training and testing. In total, 47,060 individuals (mean age ± standard deviation, 38.7 ± 11.9 years; 22,144 males) were included. By using chronological ages as references, mean absolute error (MAE), root mean square error (RMSE), and Pearson’s correlation coefficient were used to assess the model performance. Summarized class activation maps were used to highlight the activated anatomical regions. The area under the curve (AUC) was used to examine the validity for sex prediction. Results: When model predictions were compared with the chronological ages, the MAE was 2.1 years, RMSE was 2.8 years, and Pearson’s correlation coefficient was 0.97 (p < 0.001). Cervical, thoracic spines, first ribs, aortic arch, heart, rib cage, and soft tissue of thorax and flank seemed to be the most crucial activated regions in the age prediction model. The sex prediction model demonstrated an AUC of >0.99. Conclusion: Deep learning can accurately estimate age and sex based on CXRs. MDPI 2021-09-27 /pmc/articles/PMC8509558/ /pubmed/34640449 http://dx.doi.org/10.3390/jcm10194431 Text en © 2021 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Yang, Chung-Yi Pan, Yi-Ju Chou, Yen Yang, Chia-Jung Kao, Ching-Chung Huang, Kuan-Chieh Chang, Jing-Shan Chen, Hung-Chieh Kuo, Kuei-Hong Using Deep Neural Networks for Predicting Age and Sex in Healthy Adult Chest Radiographs |
title | Using Deep Neural Networks for Predicting Age and Sex in Healthy Adult Chest Radiographs |
title_full | Using Deep Neural Networks for Predicting Age and Sex in Healthy Adult Chest Radiographs |
title_fullStr | Using Deep Neural Networks for Predicting Age and Sex in Healthy Adult Chest Radiographs |
title_full_unstemmed | Using Deep Neural Networks for Predicting Age and Sex in Healthy Adult Chest Radiographs |
title_short | Using Deep Neural Networks for Predicting Age and Sex in Healthy Adult Chest Radiographs |
title_sort | using deep neural networks for predicting age and sex in healthy adult chest radiographs |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8509558/ https://www.ncbi.nlm.nih.gov/pubmed/34640449 http://dx.doi.org/10.3390/jcm10194431 |
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