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With or without human interference for precise age estimation based on machine learning?

International Journal of Legal Medicine volume 136, pages 821–831 (2022)Cite this article

Abstract

Age estimation can aid in forensic medicine applications, diagnosis, and treatment planning for orthodontics and pediatrics. Existing dental age estimation methods rely heavily on specialized knowledge and are highly subjective, wasting time, and energy, which can be perfectly solved by machine learning techniques. As the key factor affecting the performance of machine learning models, there are usually two methods for feature extraction: human interference and autonomous extraction without human interference. However, previous studies have rarely applied these two methods for feature extraction in the same image analysis task. Herein, we present two types of convolutional neural networks (CNNs) for dental age estimation. One is an automated dental stage evaluation model (ADSE model) based on specified manually defined features, and the other is an automated end-to-end dental age estimation model (ADAE model), which autonomously extracts potential features for dental age estimation. Although the mean absolute error (MAE) of the ADSE model for stage classification is 0.17 stages, its accuracy in dental age estimation is unsatisfactory, with the MAE (1.63 years) being only 0.04 years lower than the manual dental age estimation method (MDAE model). However, the MAE of the ADAE model is 0.83 years, being reduced by half that of the MDAE model. The results show that fully automated feature extraction in a deep learning model without human interference performs better in dental age estimation, prominently increasing the accuracy and objectivity. This indicates that without human interference, machine learning may perform better in the application of medical imaging.

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Funding

This work was supported by the National Key Research and Development Program of China under Grant No. 2017YFA0700800; the National Natural Science Foundation of China under Grant Nos. 81701869, 61971343, and 61627811; the Key Research and Development Program of Shaanxi Province of China under Grant No. 2020GXLH-Y-008; and Young Science and Technology Star Program of Shaanxi Province of China No.2020KJXX-025.

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Authors and Affiliations

  1. Key Laboratory of Shaanxi Province for Craniofacial Precision Medicine Research, College of Stomatology, Xi’an Jiaotong University, 98 XiWu Road, Xi’an, 710004, Shaanxi, People’s Republic of China

    Mengqi Han & Yu-cheng Guo

  2. Department of Orthodontics, College of Stomatology, Xi’an Jiaotong University, 98 XiWu Road, Xi’an, 710004, Shaanxi, People’s Republic of China

    Mengqi Han & Yu-cheng Guo

  3. Institute of Artificial Intelligence and Robotics, College of Artificial Intelligence, Xi’an Jiaotong University, 28 Xianning West Road, Xi’an, 710049, Shaanxi, People’s Republic of China

    Shaoyi Du, Yuyan Ge, Dong Zhang, Yuting Chi, Hong Long, Jingmin Xin, Nanning Zheng & Yu-cheng Guo

  4. College of Automation Science and Engineering, Xi’an Jiaotong University, 28 Xianning West Road, Xi’an, 710049, Shaanxi, People’s Republic of China

    Dong Zhang, Jing Yang & Yang Yang

  5. College of Medicine and Forensics, Xi’an Jiaotong University Health Science Center, 76 West Yanta Road, Xi’an, 710004, Shaanxi, People’s Republic of China

    Teng Chen

Authors
  1. Mengqi Han
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  2. Shaoyi Du
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  4. Dong Zhang
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  6. Hong Long
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  7. Jing Yang
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  8. Yang Yang
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  10. Teng Chen
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  11. Nanning Zheng
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  12. Yu-cheng Guo
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Contributions

Conceiving the study and the design: S.Y.D., Y.C.G.; annotations, creating the datasets, training the models, validation tests, conducting the statistical analysis and data analysis: M.Q.H., Y.T.C., H.L., D.Z.; analyzing the results: all authors; drafting the manuscript: M.Q.H., Y.Y.G., D.Z.; critically revising for important intellectual content: S.Y.D., Y.C.G.

Corresponding authors

Correspondence to Shaoyi Du or Yu-cheng Guo.

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Han, M., Du, S., Ge, Y. et al. With or without human interference for precise age estimation based on machine learning?. Int J Legal Med 136, 821–831 (2022). https://doi.org/10.1007/s00414-022-02796-z

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  • DOI: https://doi.org/10.1007/s00414-022-02796-z

Keywords

  • Dental age estimation
  • Demirjian method
  • Machine learning
  • Orthopantomograms