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Forensic bone age estimation of adolescent pelvis X-rays based on two-stage convolutional neural network


In the forensic estimation of bone age, the pelvis is important for identifying the bone age of teenagers. However, studies on this topic remain insufficient as a result of lower accuracy due to the overlapping of pelvic organs in X-ray images. Segmentation networks have been used to automate the location of key pelvic areas and minimize restrictions like doubling images of pelvic organs to increase the accuracy of estimation. This study conducted a retrospective analysis of 2164 pelvis X-ray images of Chinese Han teenagers ranging from 11 to 21 years old. Key areas of the pelvis were detected with a U-Net segmentation network, and the findings were combined with the original X-ray image for regional augmentation. Bone age estimation was conducted with the enhanced and not enhanced pelvis X-ray images by separately using three convolutional neural networks (CNNs). The root mean square errors (RMSE) of the Inception-V3, Inception-ResNet-V2, and VGG19 convolutional neural networks were 0.93 years, 1.12 years, and 1.14 years, respectively, and the mean absolute errors (MAE) of these networks were 0.67 years, 0.77 years, and 0.88 years, respectively. For comparison, a network without segmentation was employed to conduct the estimation, and it was found that the RMSE of the three CNNs above became 1.22 years, 1.25 years, and 1.63 years, respectively, and the MAE became 0.93 years, 0.96 years, and 1.23 years. Bland–Altman plots and attention maps were also generated to provide a visual comparison. The proposed segmentation network can be used to reduce the influence of restrictions like image overlapping of organs and can thus increase the accuracy of pelvic bone age estimation.

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The authors would like to thank letPub ( for its linguistic assistance during the preparation of this manuscript.


This work was supported by the National Natural Science Foundation of China under Grant Nos. 81571859, 81102305, and 81701869; Shanghai 2019 “Science and Technology Innovation Action Plan” technical standard project guide under Grant No. 19DZ2201300; and the Science and Technology Committee of Shanghai Municipality under Grant Nos. 17DZ2273200 and 19DZ2292700.

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Guarantor of integrity of entire study: Ya-Hui Wang and Hu Zhao; study concepts/study design or data acquisition or data analysis/interpretation: all authors; manuscript drafting or manuscript revision for important intellectual content: all authors; approval of final version of submitted manuscript: all authors; agrees to ensure any questions related to the work are appropriately resolved: all authors; literature research: Ya-Hui Wang, Li-Qin Peng, and Yu-Cheng Guo; clinical studies: Ya-Hui Wang, Hu Zhao, and Li-Qin Peng; experimental studies: Li-Qin Peng, Tai-Ang Liu, and Lei Wan and Peng Wang; statistical analysis: Li-Qin Peng and Ya-Hui Wang; and manuscript editing: Li-Qin Peng, Yu-Cheng Guo, Ya-Hui Wang, and Hu Zhao.

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Correspondence to Hu Zhao or Ya-Hui Wang.

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Ethical approval was granted by the ethics committee of the Academy of Forensic Science, Ministry of Justice, People’s Republic of China. All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Helsinki declaration and its later amendments or comparable ethical standards. Correct ethical informed consent has been obtained before our study.

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Peng, LQ., Guo, Yc., Wan, L. et al. Forensic bone age estimation of adolescent pelvis X-rays based on two-stage convolutional neural network. Int J Legal Med 136, 797–810 (2022).

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  • Bone age estimation
  • Pelvis
  • Image recognition
  • Deep learning
  • Convolutional neural networks
  • Adolescent