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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...

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Autores principales: 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
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2021
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.
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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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