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European Radiology

, Volume 29, Issue 5, pp 2322–2329 | Cite as

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

  • Yuan Li
  • Zhizhong Huang
  • Xiaoai Dong
  • Weibo Liang
  • Hui Xue
  • Lin Zhang
  • Yi ZhangEmail author
  • Zhenhua DengEmail author
Forensic Medicine

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.

Keywords

Age determination by skeleton Forensic anthropology Pelvis Radiography Machine learning 

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

Notes

Funding

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

Compliance with ethical standards

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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Copyright information

© European Society of Radiology 2018

Authors and Affiliations

  1. 1.Department of Forensic Genetics, West China School of Preclinical and Forensic MedicineSichuan UniversityChengduPeople’s Republic of China
  2. 2.Department of Forensic Pathology, West China School of Preclinical and Forensic MedicineSichuan UniversityChengdu CityPeople’s Republic of China
  3. 3.College of Computer ScienceSichuan UniversityChengduChina
  4. 4.Department of Radiology, West China HospitalSichuan UniversityChengduPeople’s Republic of China

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