Neural Network Technologies for Age Estimation in Children from Orthopantomograms (a Pilot Study)

The aim of the study was to investigate the potential of using artificial intelligence technologies for age estimation in children from dental radiographs.

Materials and Methods. A retrospective study was conducted, analyzing orthopantomograms of 322 children (173 female, 149 male) aged 4–16 years. Fourteen permanent mandibular teeth were annotated on each radiograph. Neural network training was performed by splitting the data into training and test sets at a ratio of 80:20; 5-fold cross-validation was used. Age estimation was approached as a regression task. The neural network training and validation were conducted in Python using the PyTorch library. The accuracy of age prediction was assessed using the coefficient of determination (R²), mean squared error (MSE), and mean absolute error (MAE).

Results. The study showed that the developed machine learning model was highly accurate in age estimation in children. The mean absolute error across cross-validation was 0.92 years, which was significantly lower than the error associated with traditional manual methods.

1. Zolotenkova G.V., Poletaeva M.P. Review of age assessment methods of children and adolescents by teeth X-rays. Sudebno-meditsinskaia ekspertiza 2024; 67(2): 47–52, https://doi.org/10.17116/sudmed20246702147.
2. Cummaudo M., De Angelis D., Magli F., Minà G., Merelli V., Cattaneo C. Age estimation in the living: a scoping review of population data for skeletal and dental methods. Forensic Sci Int 2021; 320: 110689, https://doi.org/10.1016/j.forsciint.2021.110689.
3. Demirjian A., Goldstein H., Tanner J.M. A new system of dental age assessment. Hum Biol 1973; 45(2): 211–227.
4. Chaillet N., Willems G., Demirjian A. Dental maturity in Belgian children using Demirjian's method and polynomial functions: new standard curves for forensic and clinical use. J Forensic Odontostomatol 2004; 22(2): 18–27.
5. Cameriere R., Ferrante L., Cingolani M. Age estimation in children by measurement of open apices in teeth. Int J Legal Med 2006; 120(1): 49–52, https://doi.org/10.1007/s00414-005-0047-9.
6. Vila-Blanco N., Varas-Quintana P., Tomás I., Carreira M.J. A systematic overview of dental methods for age assessment in living individuals: from traditional to artificial intelligence-based approaches. Int J Legal Med 2023; 137(4): 1117–1146, https://doi.org/10.1007/s00414-023-02960-z.
7. Shen S., Zhou Z., Wang J., Fan L., Han J., Tao J. Using machine learning to determine age over 16 based on development of third molar and periodontal ligament of second molar. BMC Oral Health 2023; 23(1): 680, https://doi.org/10.1186/s12903-023-03284-5.
8. Vila-Blanco N., Carreira M.J., Varas-Quintana P., Balsa-Castro C., Tomas I. Deep neural networks for chronological age estimation from OPG images. IEEE Trans Med Imaging 2020; 39(7): 2374–2384, https://doi.org/10.1109/TMI.2020.2968765.
9. Zolotenkov D.D., Ogarev E.V., Valetov D.K., Nefedova S.M., Zolotenkova G.V., Pigolkin Y.I. Age assessment using CT of knee joint and neural network technologies. Sudebno-meditsinskaia ekspertiza 2023; 66(4): 34–40, https://doi.org/10.17116/sudmed20236604134.
10. Zolotenkova G.V., Valetov D.K., Poletaeva M.P., Vassilevski Y.V. Experience of using neural networks to assess age-related changes in some structures of the skull and cervical vertebrae based on CT scans (pilot project). Sovremennye tehnologii v medicine 2024; 16(2): 29–38, https://doi.org/10.17691/stm2024.16.2.03.
11. Pigolkin Y.I., Solodovnikov V.I., Zolotenkov D.D., Salem B.R., Poletaeva M.P., Zolotenkova G.V. Experience of Data Analysis application for proceeding research data in solving the problem of target age group establishment. Sudebno-meditsinskaia ekspertiza 2024; 67(4): 37–41, https://doi.org/10.17116/sudmed20246704137.
12. Shen S., Liu Z., Wang J., Fan L., Ji F., Tao J. Machine learning assisted Cameriere method for dental age estimation. BMC Oral Health 2021; 21(1): 641, https://doi.org/10.1186/s12903-021-01996-0.
13. Huang G., Liu Z., Van Der Maaten L., Weinberger K.Q. Densely connected convolutional networks. In: 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). USA; 2017, p. 2261–2269, https://doi.org/10.1109/CVPR.2017.243.
14. Tan M., Le Q.V. EfficientNet: rethinking model scaling for convolutional neural networks. In: Proceedings of the 36th International Conference on Machine Learning, ICML. USA; 2019; p. 6105–6114. URL: https://proceedings.mlr.press/v97/tan19a.html.
15. Xu R., Lin H., Lu K., Cao L., Liu Y. A forest fire detection system based on ensemble learning. Forests 2021; 12: 217, https://doi.org/10.3390/f12020217.
16. Willmann C., Fernandez De Grado G., Kolb C., Raul J.S., Musset A.M., Gros C.I., Offner D. Accuracy of age estimation using three dental age estimation methods in a young, large, and multiethnic patient sample. Dent J (Basel) 2023; 11(12): 288, https://doi.org/10.3390/dj11120288.
17. Galibourg A., Cussat-Blanc S., Dumoncel J., Telmon N., Monsarrat P., Maret D. Comparison of different machine learning approaches to predict dental age using Demirjian's staging approach. Int J Legal Med 2021; 135(2): 665–675, https://doi.org/10.1007/s00414-020-02489-5.
18. Tao J., Wang J., Wang A., Xie Z., Wang Z., Wu S., Hassanien A.E., Xiao K. Dental age estimation: a machine learning perspective. In: The International Conference on Advanced Machine Learning Technologies and Applications (AMLTA2019). Springer, Cham; 2019; p. 722–733, https://doi.org/10.1007/978-3-030-14118-9_71.
19. Abuabara A., do Nascimento T.V.P.M., da Cruz K.R., Küchler E.C., Madalena I.R., de Oliveira M.B.C.R., Lepri C.P., de Menezes-Oliveira M.A.H., de Araujo C.M., Baratto-Filho F. Dental age estimation by comparing Demirjian's method and machine learning in Southeast Brazilian youth. Forensic Sci Med Pathol 2025, https://doi.org/10.1007/s12024-025-01042-3.

Poletaeva M.P., Vasilevsky Yu.V., Valetov D.K., Angelakopoulos N., Zolotenkova G.V. Neural Network Technologies for Age Estimation in Children from Orthopantomograms (a Pilot Study). Sovremennye tehnologii v medicine 2026; 18(2): 15, https://doi.org/10.17691/stm2026.18.2.02