Advertisement

Dental Age Estimation: A Machine Learning Perspective

  • Jiang Tao
  • Jian Wang
  • Andrew Wang
  • Zhangqian Xie
  • Ziheng Wang
  • Shaozhi Wu
  • Aboul Ella Hassanien
  • Kai XiaoEmail author
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (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.

Keywords

Dental age estimation Demirjian Willem Multi-layer Perceptron 

Notes

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.

References

  1. 1.
    Williams, G.: A review of the most commonly used dental age estimation techniques. J. Forensic Odontostomatol. 19(1), 9–17 (2001)MathSciNetGoogle Scholar
  2. 2.
    Olze, A., Geserick, G., Schmeling, A.: Age estimation of unidentified corpses by measurement of root translucency. J. Forensic Odontostomatol. 22(2), 28–33 (2004)Google Scholar
  3. 3.
    Kvaal, S.I.: Collection of post mortem data: DVI protocols and quality assurance. Forensic Sci. Int. 159, S12–S14 (2006)CrossRefGoogle Scholar
  4. 4.
    Karkhanis, S., Mack, P., Franklin, D.: Dental age estimation standards for a Western Australian population. Forensic Sci. Int. 257, 509-e1 (2015)CrossRefGoogle Scholar
  5. 5.
    Ritz-Timme, S., Cattaneo, C., Collins, M.J., Waite, E.R., Schütz, H.W., Kaatsch, H.J., Borrman, H.I.M.: Age estimation: the state of the art in relation to the specific demands of forensic practise. Int. J. Legal Med. 113(3), 129–136 (2000)CrossRefGoogle Scholar
  6. 6.
    Lopez, T.T., Arruda, C.P., Rocha, M., de Oliveira Rosin, A.S.A., Michel-Crosato, E., Biazevic, M.G.H.: Estimating ages by third molars: stages of development in Brazilian young adults. J. Forensic Legal Med. 20(5), 412–418 (2013)CrossRefGoogle Scholar
  7. 7.
    Melo, M., Ata-Ali, J.: Accuracy of the estimation of dental age in comparison with chronological age in a Spanish sample of 2641 living subjects using the Demirjian and Nolla methods. Forensic Sci. Int. 270, 276-e1 (2017)CrossRefGoogle Scholar
  8. 8.
    Garn, S.M., Lewis, A.B., Kerewsky, R.S.: Genetic, nutritional, and maturational correlates of dental development. J. Dent. Res. 44(1), 228–242 (1965)CrossRefGoogle Scholar
  9. 9.
    Demirjian, A., Goldstein, H., Tanner, J.M.: A new system of dental age assessment. Hum. Biol. 45(2), 211–227 (1973)Google Scholar
  10. 10.
    Willems, G., Van Olmen, A., Spiessens, B., Carels, C.: Dental age estimation in Belgian children: Demirjian’s technique revisited. J. Forensic Sci. 46(4), 893–895 (2001)CrossRefGoogle Scholar
  11. 11.
    Ye, X., Jiang, F., Sheng, X., Huang, H., Shen, X.: Dental age assessment in 7–14-year-old Chinese children: Comparison of Demirjian and Willems methods. Forensic Sci. Int. 244, 36–41 (2014)CrossRefGoogle Scholar
  12. 12.
    Kumaresan, R., Cugati, N., Chandrasekaran, B., Karthikeyan, P.: Reliability and validity of five radiographic dental-age estimation methods in a population of Malaysian children. J. Invest. Clin. Dent. 7(1), 102–109 (2016)CrossRefGoogle Scholar
  13. 13.
    Djukic, K., Zelic, K., Milenkovic, P., Nedeljkovic, N., Djuric, M.: Dental age assessment validity of radiographic methods on Serbian children population. Forensic Sci. Int. 231(1–3), 398-e1 (2013)Google Scholar
  14. 14.
    Urzel, V., Bruzek, J.: Dental age assessment in children: a comparison of four methods in a recent French population. J. Forensic Sci. 58(5), 1341–1347 (2013)CrossRefGoogle Scholar
  15. 15.
    Nolla, C.M.: The development of the permanent teeth. J. Dent. Child. 27, 254–266 (1952)Google Scholar
  16. 16.
    Tanner, J.M.: Growth at Adolescence. Blackwell Scientific Publications, Oxford (1962)Google Scholar
  17. 17.
    Tao, J., Chen, M., Wang, J., Liu, L., Hassanien, A.E., Xiao, K.: Dental age estimation in East Asian population with least squares regression. In: International Conference on Advanced Machine Learning Technologies and Applications, pp. 653–660. Springer, Cham (2018)Google Scholar
  18. 18.
    Samuel, A.L.: Some studies in machine learning using the game of checkers. IBM J. Res. Dev. 3(3), 210–229 (1959)MathSciNetCrossRefGoogle Scholar
  19. 19.
    Kohavi, R.: A study of cross-validation and bootstrap for accuracy estimation and model selection. In: Ijcai, vol. 14, no. 2, pp. 1137–1145 (1995)Google Scholar
  20. 20.
    Altman, N.S.: An introduction to kernel and nearest-neighbor nonparametric regression. Am. Stat. 46(3), 175–185 (1992)MathSciNetGoogle Scholar
  21. 21.
    Rosenblatt, F.: Principles of neurodynamics: perceptrons and the theory of brain mechanisms (No. VG-1196-G-8). Cornell Aeronautical Lab Inc., Buffalo (1961)Google Scholar
  22. 22.
    Rummelhart, D.E.: Learning internal representations by error propagation. In: Parallel Distributed Processing: I. Foundations, pp. 318–362 (1986)Google Scholar
  23. 23.
    Cybenko, G.: Approximation by superpositions of a sigmoidal function. Math. Control Signals Systems 2(4), 303–314 (1989)MathSciNetCrossRefGoogle Scholar
  24. 24.
    Hastie, T., Tibshirani, R., Friedman, J.: The Elements of Statistical Learning, 2nd edn, pp. 587–588. Springer, New York (2008)Google Scholar
  25. 25.
    Pinkus, M.L.V.L.A., Schocken, S.: Multilayer feedforward networks with non-polynomial activation functions can approximate any continuous function. Neural Netw. 6, 861–867 (1993)CrossRefGoogle Scholar
  26. 26.
    Govan, A.: Introduction to optimization. In North Carolina State University, SAMSI NDHS, Undergraduate workshop (2006)Google Scholar
  27. 27.
    Breiman, L.: Bagging predictors. Mach. Learn. 24(2), 123–140 (1996)zbMATHGoogle Scholar
  28. 28.
    Panik, M.J.: Advanced Statistics from an Elementary Point of View, vol. 9. Academic Press, Amsterdam (2005)zbMATHGoogle Scholar
  29. 29.
    Lehmann, E.L., Casella, G.: Theory of Point Estimation. Springer, Heidelberg (2006)zbMATHGoogle Scholar
  30. 30.
    Mood, A.M., Graybill, F.A., Boes, D.C.: Introduction to the Theory of Statistics, pp. 540–541. McGraw-Hill, New York (1974)zbMATHGoogle Scholar
  31. 31.
    Nasrabadi, N.M.: Pattern recognition and machine learning. J. Electron. Imaging 16(4), 049901 (2007)MathSciNetCrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  • Jiang Tao
    • 1
  • Jian Wang
    • 1
  • Andrew Wang
    • 2
  • Zhangqian Xie
    • 3
  • Ziheng Wang
    • 4
  • Shaozhi Wu
    • 5
  • Aboul Ella Hassanien
    • 6
  • Kai Xiao
    • 7
    Email author
  1. 1.Department oft of General DentistryNinth People’s Hospital, Shanghai Jiao Tong University School of MedicineShanghaiChina
  2. 2.Department of Electrical Engineering and Computer SciencesUniversity of CaliforniaBerkeleyUSA
  3. 3.School of MathematicsShandong UniversityJinanChina
  4. 4.The School of Aerospace Engineering and Applied MechanicsTongji UniversityShanghaiChina
  5. 5.School of Computer Science and EngineeringUniversity of Electronic Science and Technology of ChinaChengduChina
  6. 6.Information Technology DepartmentCairo UniversityCairoEgypt
  7. 7.School of Electronic Information and Electrical EngineeringShanghai Jiao Tong UniversityShanghaiChina

Personalised recommendations