Skip to main content

Advertisement

SpringerLink
Log in

An automatic methodology for full dentition maturity staging from OPG images using deep learning

  • Published:
Applied Intelligence Aims and scope Submit manuscript

Abstract

Accurate maturity identification of permanent teeth for children and adolescents using orthopantomogram (OPG) images is crucial in pediatric dentistry.Although recent advancements in deep learning have shown promising outcomes in maturity staging of third molars and mandibular permanent dentition, limited research has focused on assessing the entire dentition. Furthermore, accurate performance is hindered due to variations in tooth appearance and similarities between adjacent stages. This paper develops an automatic methodology to identify the tooth maturity stages of the full permanent dentition. Specifically, a tooth localization model based on YOLOv3 is presented to detect and number each candidate permanent tooth. Subsequently, we propose a novel symmetric and ordinal staging network (SOS-Net) for the precise maturity identification of each tooth. SOS-Net incorporates a symmetric feature combination block (SFC Block) based on bilateral facial symmetry to enhance discriminative feature representation in order to improve performance, while minimizing increases in model parameters. Additionally, we introduce an auxiliary regression branch with a novel loss called adjacent stage aware (ASA) loss to provide information in the order of maturity levels and effectively reduce misclassification between adjacent stages. The proposed methodology is evaluated on a private OPG image dataset collected from individuals aged 3 to 14 years. The ablation studies and visual analysis of SOS-Net verified the effectiveness and interpretability of the SFC block and ASA loss, with a F1-score improvement of \(3.77\%\). Moreover, comparative experiments demonstrate the good performance of the proposed methodology compared to state-of-the-art methods in maturity staging and age estimation.

Graphical abstract

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13
Fig. 14
Fig. 15
Fig. 16
Fig. 17

Similar content being viewed by others

Data Availability

The data that support the findings of this study are available on request from the corresponding author. The data are not publicly available due to privacy or ethical restrictions.

References

  1. Chalakkal P, De Souza N, de Ataide IDN, Akkara F, Chandran R (2021) The resistance of succedaneous teeth to periapical infection: a series of seven cases. Contemp Clin Dent 12(1):88. https://doi.org/10.4103/ccd.ccd_68_20

  2. Chen D, Li X, Lu F, Wang Y, Xiong F, Li Q (2019) Dentin dysplasia type I-A dental disease with genetic heterogeneity. Oral Dis 25(2):439–446. https://doi.org/10.1111/odi.12861

  3. Bianchi, ML, Bishop, N., Guañabens, N, Hofmann, C, Jakob, F, Roux, C, Zillikens, M, Of the European Calcified Tissue Society, R.B.D.A.G (2020) Hypophosphatasia in adolescents and adults: overview of diagnosis and treatment. Osteoporosis International 31:1445–1460. https://doi.org/10.1007/s00198-020-05345-9

  4. Mollabashi V, Yousefi F, Gharebabaei L, Amini P (2019) The relation between dental age and cervical vertebral maturation in orthodontic patients aged 8 to 16 years: a cross-sectional study. Int Orthod 17(4):710–718. https://doi.org/10.1016/j.ortho.2019.08.009

    Article  Google Scholar 

  5. Koçak T, Akan B (2021) Assessment of maturation indicators in individuals with different skeletal malocclusion. Journal of Orofacial Orthopedics/- Fortschritte der Kieferorthopädie 82(3):187–197. https://doi.org/10.1007/s00056-021-00286-2

    Article  Google Scholar 

  6. Demirjian A, Goldstein H, Tanner JM (1973) A new system of dental age assessment. Human biology, 211–227

  7. Li Y, Huang Z, Dong X, Liang W, Xue H, Zhang L, Zhang Y, Deng Z (2019) Forensic age estimation for pelvic x-ray images using deep learning. Eur Radiol 29(5):2322–2329. https://doi.org/10.1007/s00330-018-5791-6

    Article  Google Scholar 

  8. Zhang Y, Davison BD (2021) Adversarial regression learning for bone age estimation. In: International conference on information processing in medical imaging, pp 742–754. https://doi.org/10.1007/978-3-030-78191-0_57. Springer

  9. Vila-Blanco N, Carreira MJ, Varas-Quintana P, Balsa-Castro C, Tomas I (2020) Deep neural networks for chronological age estimation from opg images. IEEE Trans Med Imaging 39(7):2374–2384. https://doi.org/10.1109/TMI.2020.2968765

    Article  Google Scholar 

  10. Guo Y, Han M, Chi Y, Long H, Zhang D, Yang J, Yang Y, Chen T, Du S (2021) Accurate age classification using manual method and deep convolutional neural network based on orthopantomogram images. Int J Legal Med 135(4):1589–1597. https://doi.org/10.1007/s00414-021-02542-x

    Article  Google Scholar 

  11. Miloěević D, Vodanović M, Galić I, Subašić M (2022) Automated estimation of chronological age from panoramic dental x-ray images using deep learning. Expert Syst Appl 189:116038. https://doi.org/10.1016/j.eswa.2021.116038

    Article  Google Scholar 

  12. Zhang D, Yang J, Du S, Bu W, Guo Y-C (2023) An uncertainty-aware and sex-prior guided biological age estimation from orthopantomogram images. IEEE J Biomed Health Inform 1–12. https://doi.org/10.1109/JBHI.2023.3297610

  13. Mohammad N, Muad AM, Ahmad R, Yusof MYPM (2021) Reclassification of demirjian’s mandibular premolars staging for age estimation based on semi-automated segmentation of deep convolutional neural network. Forensic Imaging 24:200440. https://doi.org/10.1016/j.fri.2021.200440

    Article  Google Scholar 

  14. Upalananda, W, Wantanajittikul, K, Na Lampang, S, Janhom, A (2021) Semi-automated technique to assess the developmental stage of mandibular third molars for age estimation. Aust J Forensic Sci 1–11. https://doi.org/10.1080/00450618.2021.1882570

  15. Galibourg A, Cussat-Blanc S, Dumoncel J, Telmon N, Monsarrat P, Maret D (2021) Comparison of different machine learning approaches to predict dental age using demirjian’s staging approach. Int J Legal Med 135:665–675. https://doi.org/10.1007/s00414-020-02489-5

    Article  Google Scholar 

  16. Tao J, Wang J, Wang A, Xie Z, Wang Z, Wu S, Hassanien AE, Xiao K (2020) Dental age estimation: a machine learning perspective. In: The International conference on advanced machine learning technologies and applications (AMLTA2019) 4, pp 722–733. https://doi.org/10.1007/978-3-030-14118-9_71. Springer

  17. Banar N, Bertels J, Laurent F, Boedi RM, De Tobel J, Thevissen P, Vandermeulen D (2020) Towards fully automated third molar development staging in panoramic radiographs. Int J Legal Med 134(5):1831–1841. https://doi.org/10.1007/s00414-020-02283-3

    Article  Google Scholar 

  18. Han M, Du S, Ge Y, Zhang D, Chi Y, Long H, Yang J, Yang Y, Xin J, Chen T et al (2022) With or without human interference for precise age estimation based on machine learning? Int J Legal Med 1–11. https://doi.org/10.1007/s00414-022-02796-z

  19. Jin K, Yan Y, Chen M, Wang J, Pan X, Liu X, Liu M, Lou L, Wang Y, Ye J (2022) Multimodal deep learning with feature level fusion for identification of choroidal neovascularization activity in age-related macular degeneration. Acta Ophthalmol 100(2):512–520. https://doi.org/10.1111/aos.14928

  20. Song W, Li S, Fang L, Lu T (2018) Hyperspectral image classification with deep feature fusion network. IEEE Trans Geosci Remote Sens 56(6):3173–3184. https://doi.org/10.1109/TGRS.2018.2794326

  21. Hu J, Chen Y, Zhong J, Ju R, Yi Z (2018) Automated analysis for retinopathy of prematurity by deep neural networks. IEEE transactions on medical imaging 38(1):269–279. https://doi.org/10.1109/TMI.2018.2863562

    Article  Google Scholar 

  22. Peng Y, Zhu W, Chen Z, Wang M, Geng L, Yu K, Zhou Y, Wang T, Xiang D, Chen F, Xinjian C (2021) Automatic staging for retinopathy of prematurity with deep feature fusion and ordinal classification strategy. IEEE Trans Med Imaging 40(7):1750–1762. https://doi.org/10.1109/TMI.2021.3065753

  23. Chen L, Wang X, Zhu Y, Nie R (2022) Multi-level difference information replenishment for medical image fusion. Appl Intell 1–13. https://doi.org/10.1007/s10489-022-03819-3

  24. Galdran A, Dolz J, Chakor H, Lombaert H, Ben Ayed I (2020) Costsensitive regularization for diabetic retinopathy grading from eye fundus images. In: International conference on medical image computing and computer-assisted intervention, pp 665–674. https://doi.org/10.1007/978-3-030-59722-1_64. Springer

  25. Vuong TLT, Lee D, Kwak JT, Kim K (2020) Multi-task deep learning for colon cancer grading. In: 2020 International conference on electronics, information, and communication (ICEIC), pp 1–2. https://doi.org/10.1109/ICEIC49074.2020.9051305. IEEE

  26. Liu S, Gong L, Ma K, Zheng Y (2020) Green: a graph residual re-ranking network for grading diabetic retinopathy. In: International conference on medical image computing and computer-assisted intervention, pp 585–594. https://doi.org/10.1007/978-3-030-59722-1_56. Springer

  27. Redmon J, Farhadi A (2018) Yolov3: An incremental improvement. arXiv:1804.02767. https://doi.org/10.48550/arXiv.1804.02767

  28. Park J, Lee Y (2022) Oriented-tooth recognition using a five-axis objectdetection approach. Appl Intell 1–12. https://doi.org/10.1007/s10489-022-03544-x

  29. Peck S, Peck L (1993) A time for change of tooth numbering systems. J Dent Educ 57(8):643–647

  30. Li X, Ding M, Pižurica A (2019) Deep feature fusion via two-stream convolutional neural network for hyperspectral image classification. IEEE Trans Geosci Remote Sens 58(4):2615–2629. https://doi.org/10.1109/TGRS.2019.2952758

  31. Khan MA, Sarfraz MS, Alhaisoni M, Albesher AA, Wang S, Ashraf I (2020) Stomachnet: optimal deep learning features fusion for stomach abnormalities classification. IEEE Access 8:197969–197981. https://doi.org/10.1109/ACCESS.2020.3034217

  32. Marks SC Jr, Gorski JP, Wise GE (1995) The mechanisms and mediators of tooth eruption-models for developmental biologists. Int J Dev Biol 39:223–230. https://doi.org/10.1387/ijdb.7626410

  33. Heldring N, Larsson A, Rezaie A-R, Råsten-Almqvist P, Zilg B (2022) A probability model for assessing age relative to the 18-year old threshold based on magnetic resonance imaging of the knee combined with radiography of third molars in the lower jaw. Forensic Sci Int 330:111108. https://doi.org/10.1016/j.forsciint.2021.111108

  34. He K, Zhang X, Ren S, Sun J (2016) Deep residual learning for image recognition. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 770–778

  35. Szegedy C, Vanhoucke V, Ioffe S, Shlens J, Wojna Z (2016) Rethinking the inception architecture for computer vision. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 2818–2826

  36. Xie S, Girshick R, Dollár P, Tu Z, He K (2017) Aggregated residual transformations for deep neural networks. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 1492–1500

  37. Liu S, Johns E, Davison AJ (2019) End-to-end multi-task learning with attention. In: Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp 1871–1880

  38. Saranya K, Ponnada SR, Cheruvathoor JJ, Jacob S, Kandukuri G, Mudigonda M, Kasabu AL, Balla SB (2021) Assessing the probability of having attained 16 years of age in juveniles using third molar development in a sample of south indian population. J Forensic Odontostomatol 39(1):16

  39. Selvaraju RR, Cogswell M, Das A, Vedantam R, Parikh D, Batra D (2017) Grad-cam: visual explanations from deep networks via gradient-based localization. In: Proceedings of the IEEE international conference on computer vision, pp 618–626

  40. Roberts G, Parekh S, Petrie A, Lucas V (2008) Dental age assessment (DAA): a simple method for children and emerging adults. Br Dent J 204(4):7. https://doi.org/10.1038/bdj.2008.21

    Article  Google Scholar 

  41. Han M-Q, Jia S-X, Wang C-X, Chu G, Chen T, Zhou H, Guo Y-C (2020) Accuracy of the demirjian, willems and nolla methods for dental age estimation in a northern chinese population. Archives of Oral Biology 118:104875. https://doi.org/10.1016/j.archoralbio.2020.104875

  42. Ozveren N, Serindere G (2018) Comparison of the applicability of demirjian and willems methods for dental age estimation in children from the thrace region, turkey. Forensic Sci Int 285:38–43. https://doi.org/10.1016/j.forsciint.2018.01.017

Download references

Acknowledgements

This work was supported by the National Natural Science Foundation of China under Grant No.62376176 and No.62206189, the Science and Technology Department of Sichuan Province under Grant No. 2023YFG0272, the China Postdoctoral Science Foundation under Grant No.2023M732427, and the Exploration and \( R \& D\) Project of West China Hospital of Stomatology, Sichuan University under Grant No. LCYJ2019-9.

Author information

Authors and Affiliations

  1. Machine Intelligence Laboratory, College of Computer Science, Sichuan University, Chengdu, 610065, People’s Republic of China

    Wenxuan Dong, Tao He & Jixiang Guo

  2. Department of Medical Imaging, National Clinical Research Center of Oral Disease, West China Hospital of Stomatology, Sichuan University, Chengdu, 610041, People’s Republic of China

    Meng You, Yueting Tang & Yuchao Shi

  3. Department of Oral Radiology, Sichuan Hospital of Stomatology, Chengdu, 610015, People’s Republic of China

    Jiaqi Dai

Authors
  1. Wenxuan Dong
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Meng You
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Tao He
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Jiaqi Dai
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Yueting Tang
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Yuchao Shi
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Jixiang Guo
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Jixiang Guo.

Ethics declarations

Competing interests

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Dong, W., You, M., He, T. et al. An automatic methodology for full dentition maturity staging from OPG images using deep learning. Appl Intell 53, 29514–29536 (2023). https://doi.org/10.1007/s10489-023-05096-0

Download citation

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10489-023-05096-0

Keywords

Search

Navigation