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Dental Age Estimation: A Machine Learning Perspective

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The International Conference on Advanced Machine Learning Technologies and Applications (AMLTA2019) (AMLTA 2019)

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 921))

Abstract

Dental age estimation is important for determining the actual age of an individual. In this paper, for the purpose of improving the accuracy of dental age estimation, we present several machine learning algorithms. We apply Demirjian’s method, Willem’s method, and our methods to a dataset with 1636 cases; 787 males and 849 females. The Multi-layer Perceptron algorithm is used to predict dental age in our experiments. In order to avoid overfitting, we use Leave-one-out cross-validation when training the model. Meanwhile, we employ root-mean-square error, mean-square-error and mean-absolute-error to measure the error of the results. Through experiments, we verify that this algorithm is more accurate than traditional dental methods. In addition, we try to use a new set of features that are converted by traditional dental methods. Specifically, we find that using Demirjian’s method converted data for males and using Willem’s method converted data for females can improve the accuracy of the dental age predictions.

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Acknowledgments

The data in this paper is provided by the Ninth People’s Hospital affiliated to Shanghai Jiao Tong University School of Medicine. We also sincerely thank 1636 volunteers who have supplied the collected dental data for research.

Author information

Authors and Affiliations

  1. Department oft of General Dentistry, Ninth People’s Hospital, Shanghai Jiao Tong University School of Medicine, No. 639 Zhi Zao Ju Road, Shanghai, 200011, China

    Jiang Tao & Jian Wang

  2. Department of Electrical Engineering and Computer Sciences, University of California, Berkeley Soda Hall, Berkeley, CA, 94720-1776, USA

    Andrew Wang

  3. School of Mathematics, Shandong University, 27 Shanda Nanlu, Jinan, 250100, China

    Zhangqian Xie

  4. The School of Aerospace Engineering and Applied Mechanics, Tongji University, No. 1239 Siping Road, Shanghai, 200000, China

    Ziheng Wang

  5. School of Computer Science and Engineering, University of Electronic Science and Technology of China, No. 4, Section 2, North Jianshe Road, Chengdu, 610054, China

    Shaozhi Wu

  6. Information Technology Department, Cairo University, 5 Ahmed Zewal Street, Orman, Giza, Cairo, Egypt

    Aboul Ella Hassanien

  7. School of Electronic Information and Electrical Engineering, Shanghai Jiao Tong University, 800 Dongchuan Road, Shanghai, 200240, China

    Kai Xiao

Authors
  1. Jiang Tao
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  2. Jian Wang
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  3. Andrew Wang
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  4. Zhangqian Xie
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  5. Ziheng Wang
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  6. Shaozhi Wu
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  7. Aboul Ella Hassanien
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  8. Kai Xiao
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Corresponding author

Correspondence to Kai Xiao .

Editor information

Editors and Affiliations

  1. Faculty of Computers and Information, Cairo University, Giza, Egypt

    Aboul Ella Hassanien

  2. Faculty of Computers and Information, Benha University, Benha, Egypt

    Ahmad Taher Azar

  3. School of Computing, Science and Engineering, University of Salford, Salford, Greater Manchester, UK

    Tarek Gaber

  4. Department of Computer Science and Engineering, School of Computing and IT, Faculty of Engineering, Manipal University Jaipur, Jaipur, Rajasthan, India

    Roheet Bhatnagar

  5. Faculty of Computer and Information Science, Ain Shams University, Cairo, Egypt

    Mohamed F. Tolba

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Tao, J. et al. (2020). Dental Age Estimation: A Machine Learning Perspective. In: Hassanien, A., Azar, A., Gaber, T., Bhatnagar, R., F. Tolba, M. (eds) The International Conference on Advanced Machine Learning Technologies and Applications (AMLTA2019). AMLTA 2019. Advances in Intelligent Systems and Computing, vol 921. Springer, Cham. https://doi.org/10.1007/978-3-030-14118-9_71

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