Abstract
Purpose
To develop and test the performance of deep convolutional neural networks (DCNNs) for automated classification of age and sex on chest radiographs (CXR).
Methods
We obtained 112,120 frontal CXRs from the NIH ChestX-ray14 database performed in 48,780 females (44%) and 63,340 males (56%) ranging from 1 to 95 years old. The dataset was split into training (70%), validation (10%), and test (20%) datasets, and used to fine-tune ResNet-18 DCNNs pretrained on ImageNet for (1) determination of sex (using entire dataset and only pediatric CXRs); (2) determination of age < 18 years old or ≥ 18 years old (using entire dataset); and (3) determination of age < 11 years old or 11–18 years old (using only pediatric CXRs). External testing was performed on 662 CXRs from China. Area under the receiver operating characteristic curve (AUC) was used to evaluate DCNN test performance.
Results
DCNNs trained to determine sex on the entire dataset and pediatric CXRs only had AUCs of 1.0 and 0.91, respectively (p < 0.0001). DCNNs trained to determine age < or ≥ 18 years old and < 11 vs. 11–18 years old had AUCs of 0.99 and 0.96 (p < 0.0001), respectively. External testing showed AUC of 0.98 for sex (p = 0.01) and 0.91 for determining age < or ≥ 18 years old (p < 0.001).
Conclusion
DCNNs can accurately predict sex from CXRs and distinguish between adult and pediatric patients in both American and Chinese populations. The ability to glean demographic information from CXRs may aid forensic investigations, as well as help identify novel anatomic landmarks for sex and age.
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Data availability
The source data utilized in this study is publicly available.
Code availability
Our code utilizes standard methodologies and software packages that are described in the manuscript to allow independent replication.
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Acknowledgements
We thank Tae Soo Kim, MSE, for technical advising.
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Yi, P.H., Wei, J., Kim, T.K. et al. Radiology “forensics”: determination of age and sex from chest radiographs using deep learning . Emerg Radiol 28, 949–954 (2021). https://doi.org/10.1007/s10140-021-01953-y
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DOI: https://doi.org/10.1007/s10140-021-01953-y