Using deep neural networks for predicting age and sex in healthy adult chest radiographs
Journal of Clinical Medicine, 2021•mdpi.com
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 …
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 …
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.![](/scholar/images/qa_favicons/mdpi.com.png)
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