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Forensic age estimation for pelvic X-ray images using deep learning

  • Forensic Medicine
  • Published:
European Radiology Aims and scope Submit manuscript

Abstract

Purpose

To develop a deep learning bone age assessment model based on pelvic radiographs for forensic age estimation and compare its performance to that of the existing cubic regression model.

Materials and method

A retrospective collection data of 1875 clinical pelvic radiographs between 10 and 25 years of age was obtained to develop the model. Model performance was assessed by comparing the testing results to estimated ages calculated directly using the existing cubic regression model based on ossification staging methods. The mean absolute error (MAE) and root-mean-squared error (RMSE) between the estimated ages and chronological age were calculated for both models.

Results

For all test samples (between 10 and 25 years old), the mean MAE and RMSE between the automatic estimates using the proposed deep learning model and the reference standard were 0.94 and 1.30 years, respectively. For the test samples comparable to those of the existing cubic regression model (between 14 and 22 years old), the mean MAE and RMSE for the deep learning model were 0.89 and 1.21 years, respectively. For the existing cubic regression model, the mean MAE and RMSE were 1.05 and 1.61 years, respectively.

Conclusion

The deep learning convolutional neural network model achieves performance on par with the existing cubic regression model, demonstrating predictive ability capable of automated skeletal bone assessment based on pelvic radiographic images.

Key Points

• The pelvis has considerable value in determining the bone age.

• Deep learning can be used to create an automated bone age assessment model based on pelvic radiographs.

• The deep learning convolutional neural network model achieves performance on par with the existing cubic regression model.

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Abbreviations

CA:

Chronological age

CNN:

Convolutional neural network

EBA-CNN:

Bone age estimated by the CNN

EBA-CR:

Bone age calculated by the cubic regression model

ICA:

Ossification centre of the iliac crest

IW:

Iliac wing

KK-SM:

Kreitner and Kellinghaus ossification staging methods

MAE:

Mean absolute difference

RMSE:

Root-mean-squared error

ROC:

Receiver operating characteristic

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Funding

The authors state that this work has not received any funding.

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Authors and Affiliations

Authors

Corresponding authors

Correspondence to Yi Zhang or Zhenhua Deng.

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Guarantor

The scientific guarantor of this publication is Zhen-hua Deng.

Conflict of interest

The authors declare that they have no conflict of interest.

Statistics and biometry

No complex statistical methods were necessary for this paper.

Informed consent

Informed consent was waived.

Ethical approval

This study was performed with the approval of the ethics committee of the West China Hospital of Sichuan University.

Study subjects or cohorts overlap

Some study subjects or cohorts have been previously reported in Zhang K, Dong XA, Fan F, Deng ZH (2016) Age estimation based on pelvic ossification using regression models from conventional radiography. International Journal of Legal Medicine 130:1143–1148.

Methodology

• Diagnostic or prognostic study

• Performed at one institution

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Cite this article

Li, Y., Huang, Z., Dong, X. et al. Forensic age estimation for pelvic X-ray images using deep learning. Eur Radiol 29, 2322–2329 (2019). https://doi.org/10.1007/s00330-018-5791-6

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  • DOI: https://doi.org/10.1007/s00330-018-5791-6

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