Skip to main content

Automatic and Interpretable Model for Periodontitis Diagnosis in Panoramic Radiographs

  • 6061 Accesses

Part of the Lecture Notes in Computer Science book series (LNIP,volume 12262)

Abstract

Periodontitis is a prevalent and irreversible chronic inflammatory disease both in developed and developing countries, and affects about 20%-50% of the global population. The tool for automatically diagnosing periodontitis is highly demanded to screen at-risk people for periodontitis and its early detection could prevent the onset of tooth loss, especially in local community and health care settings with limited dental professionals. In the medical field, doctors need to understand and trust the decisions made by computational models and developing interpretable machine learning models is crucial for disease diagnosis. Based on these considerations, we propose an interpretable machine learning method called Deetal-Perio to predict the severity degree of periodontitis in dental panoramic radiographs. In our method, alveolar bone loss (ABL), the clinical hallmark for periodontitis diagnosis, could be interpreted as the key feature. To calculate ABL, we also propose a method for teeth numbering and segmentation. First, Deetal-Perio segments and indexes the individual tooth via Mask R-CNN combined with a novel calibration method. Next, Deetal-Perio segments the contour of the alveolar bone and calculates a ratio for individual tooth to represent ABL. Finally, Deetal-Perio predicts the severity degree of periodontitis given the ratios of all the teeth. The entire architecture could not only outperform state-of-the-art methods and show robustness on two data sets in both periodontitis prediction, and teeth numbering and segmentation tasks, but also be interpretable for doctors to understand the reason why Deetal-Perio works so well.

Keywords

  • Teeth segmentation and numbering
  • Interpretable machine learning
  • Periodontitis diagnosis
  • Panoramic radiograph

This is a preview of subscription content, access via your institution.

Buying options

Chapter
USD   29.95
Price excludes VAT (USA)
  • DOI: 10.1007/978-3-030-59713-9_44
  • Chapter length: 10 pages
  • Instant PDF download
  • Readable on all devices
  • Own it forever
  • Exclusive offer for individuals only
  • Tax calculation will be finalised during checkout
eBook
USD   109.00
Price excludes VAT (USA)
  • ISBN: 978-3-030-59713-9
  • Instant PDF download
  • Readable on all devices
  • Own it forever
  • Exclusive offer for individuals only
  • Tax calculation will be finalised during checkout
Softcover Book
USD   149.99
Price excludes VAT (USA)
Fig. 1.
Fig. 2.
Fig. 3.
Fig. 4.

References

  1. Balaei, A.T., de Chazal, P., Eberhard, J., Domnisch, H., Spahr, A., Ruiz, K.: Automatic detection of periodontitis using intra-oral images. In: 2017 39th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3906–3909 (2017). https://doi.org/10.1109/EMBC.2017.8037710

  2. Bhatt, A.A., et al.: Contributors. In: Cappelli, D.P., Mobley, C.C. (eds.) Prevention in Clinical Oral Health Care, pp. v–vi. Mosby, Saint Louis (2008). https://doi.org/10.1016/B978-0-323-03695-5.50001-X, http://www.sciencedirect.com/science/article/pii/B978032303695550001X

  3. Chawla, N.V., Bowyer, K.W., Hall, L.O., Kegelmeyer, W.P.: Smote: synthetic minority over-sampling technique. J. Artif. Intell. Res. 16, 321–357 (2002)

    CrossRef  Google Scholar 

  4. Chen, H., et al.: A deep learning approach to automatic teeth detection and numbering based on object detection in dental periapical films. Sci. Rep. 9(1),  3840 (2019). https://doi.org/10.1038/s41598-019-40414-y

  5. Chen, T., Guestrin, C.: Xgboost: a scalable tree boosting system. In: Proceedings of the 22nd ACM Sigkdd International Conference on Knowledge Discovery and Data Mining, pp. 785–794. ACM (2016)

    Google Scholar 

  6. Cui, Z., Li, C., Wang, W.: ToothNet: automatic tooth instance segmentation and identification from cone beam CT images. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 6368–6377 (2019)

    Google Scholar 

  7. He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask R-CNN. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017)

    Google Scholar 

  8. Joo, J., Jeong, S., Jin, H., Lee, U., Yoon, J.Y., Kim, S.C.: Periodontal disease detection using convolutional neural networks. In: 2019 International Conference on Artificial Intelligence in Information and Communication (ICAIIC), pp. 360–362 (2019). https://doi.org/10.1109/ICAIIC.2019.8669021

  9. Li, H., et al.: Modern deep learning in bioinformatics. J. Mol. Cell Biol., June 2020. https://doi.org/10.1093/jmcb/mjaa030, mjaa030

  10. Li, Y., Huang, C., Ding, L., Li, Z., Pan, Y., Gao, X.: Deep learning in bioinformatics: Introduction, application, and perspective in the big data era. Methods 166, 4–21 (2019)

    CrossRef  Google Scholar 

  11. Lindhe, J., et al.: Consensus report: chronic periodontitis. Ann. Periodontol. 4(1), 38 (1999)

    CrossRef  Google Scholar 

  12. Nazir, M.A.: Prevalence of periodontal disease, its association with systemic diseases and prevention. Int. J. Health Sci. 11(2), 72–80 (2017). https://www.ncbi.nlm.nih.gov/pubmed/28539867, www.ncbi.nlm.nih.gov/pmc/articles/PMC5426403/

  13. Ozden, F.O., Ozgonenel, O., Ozden, B., Aydogdu, A.: Diagnosis of periodontal diseases using different classification algorithms: a preliminary study. Niger. J. Clin. Practice 18(3), 416–421 (2015)

    CrossRef  Google Scholar 

  14. de Pablo, P., Chapple, I.L.C., Buckley, C.D., Dietrich, T.: Periodontitis in systemic rheumatic diseases. Nature Rev. Rheumatol. 5(4), 218–224 (2009). https://doi.org/10.1038/nrrheum.2009.28

  15. Volkovs, M., Yu, G.W., Poutanen, T.: Content-based neighbor models for cold start in recommender systems. Proc. Recommender Syst. Challenge 2017, 1–6 (2017)

    Google Scholar 

  16. Wirtz, A., Mirashi, S.G., Wesarg, S.: Automatic teeth segmentation in panoramic X-Ray images using a coupled shape model in combination with a neural network. In: Frangi, A.F., Schnabel, J.A., Davatzikos, C., Alberola-López, C., Fichtinger, G. (eds.) MICCAI 2018. LNCS, vol. 11073, pp. 712–719. Springer, Cham (2018). https://doi.org/10.1007/978-3-030-00937-3_81

    CrossRef  Google Scholar 

  17. Yang, M., Nam, G.E., Salamati, A., Baldwin, M., Deng, M., Liu, Z.J.: Alveolar bone loss and mineralization in the pig with experimental periodontal disease. Heliyon 4(3), e00589 (2018)

    CrossRef  Google Scholar 

  18. Zhou, L., et al.: A rapid, accurate and machine-agnostic segmentation and quantification method for CT-based covid-19 diagnosis. IEEE Trans. Med. Imaging 99, 1 (2020)

    Google Scholar 

Download references

Acknowledgement

We thank He Zhang, Yi Zhang, and Yongwei Tan at Suzhou Stomatological Hospital for providing the data. The research reported in this publication was supported by the King Abdullah University of Science and Technology (KAUST) Office of Sponsored Research (OSR) under Award No. FCC/1/1976-04, FCC/1/1976-06, FCC/1/1976-17, FCC/1/1976-18, FCC/1/1976-23, FCC/1/1976-25, FCC/1/1976-26, and REI/1/0018-01-01.

Author information

Authors and Affiliations

Authors

Corresponding authors

Correspondence to Jieyu Chen , Feng Gao , Ying Xu or Xin Gao .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and Permissions

Copyright information

© 2020 Springer Nature Switzerland AG

About this paper

Verify currency and authenticity via CrossMark

Cite this paper

Li, H. et al. (2020). Automatic and Interpretable Model for Periodontitis Diagnosis in Panoramic Radiographs. In: , et al. Medical Image Computing and Computer Assisted Intervention – MICCAI 2020. MICCAI 2020. Lecture Notes in Computer Science(), vol 12262. Springer, Cham. https://doi.org/10.1007/978-3-030-59713-9_44

Download citation

  • DOI: https://doi.org/10.1007/978-3-030-59713-9_44

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-59712-2

  • Online ISBN: 978-3-030-59713-9

  • eBook Packages: Computer ScienceComputer Science (R0)