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)


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.


Dental age estimation Demirjian Willem Multi-layer Perceptron 



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.


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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

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