Identification and quantification of caries from CBCT segmented images

  • Luiz G. K. Zanini USP
  • Fátima L. S. Nunes USP
  • Izabel R. F. Rubira-Bullen USP

Resumo


Interproximal caries is a bacterial infection that occurs in the oral cavity, causing structural lesions between teeth. Diagnosis typically involves using radiographic techniques to capture images, but the use of Cone Beam Computed Tomography (CBCT) is still under-explored. This study explores CBCT, which acquires three-dimensional radiographic images, and employs two different image acquisition protocols to identify potential lesions. We developed a set of image processing techniques to segment three dental structures and accurately identify interproximal caries. Our results using classical metrics indicate an AUC of 0.928, a sensitivity of 87.33%, a precision of 88.50%, and a Jaccard Index of 0.7037. Our method effectively identifies lesions in dental structures, with the potential for practical assistance in diagnosing this disease.

Referências

Ahmed, S., Saifuddin, K. M., Ahmed, A. S., Aowlad Hossain, A., and Iqbal, M. T. (2017). Identification and volume estimation of dental caries using CT image. In 2017 IEEE International Conference on Telecommunications and Photonics (ICTP), pages 48–51.

Bhan, A., Goyal, A., Harsh, Chauhan, N., and Wang, C.-W. (2016). Feature line profile based automatic detection of dental caries in bitewing radiography. In 2016 International Conference on Micro-Electronics and Telecommunication Engineering (ICMETE), pages 635–640.

Braga, M. M., Mendes, F. M., and Ekstrand, K. R. (2010). Detection activity assessment and diagnosis of dental caries lesions. Dental Clinics of North America, 54(3):479–493.

Chen, R. and Zhang, H. (2017). Large-scale 3D Reconstruction with an R-based Analysis Workflow. In Proceedings of the Fourth IEEE/ACM International Conference on Big Data Computing, Applications and Technologies, BDCAT ’17, pages 85–93, New York, NY, USA. Association for Computing Machinery.

Ezhov, M., Gusarev, M., Golitsyna, M., Yates, J. M., Kushnerev, E., Tamimi, D., Aksoy, S., Shumilov, E., Sanders, A., and Orhan, K. (2021). Clinically applicable artificial intelligence system for dental diagnosis with CBCT. Scientific Reports, 11(1):15006. Number: 1 Publisher: Nature Publishing Group.

Felemban, O. M., Loo, C. Y., and Ramesh, A. (2020). Accuracy of Cone-beam Computed Tomography and Extraoral Bitewings Compared to Intraoral Bitewings in Detection of Interproximal Caries. The Journal of Contemporary Dental Practice, 21(12):1361–1367.

Gaalaas, L., Tyndall, D., Mol, A., Everett, E. T., and Bangdiwala, A. (2016). Ex vivo evaluation of new 2D and 3D dental radiographic technology for detecting caries. Dento Maxillo Facial Radiology, 45(3):20150281.

Imak, A., Celebi, A., Siddique, K., Turkoglu, M., Sengur, A., and Salam, I. (2022). Dental Caries Detection Using Score-Based Multi-Input Deep Convolutional Neural Network. IEEE Access, 10:18320–18329. Conference Name: IEEE Access.

Kumari, A. R., Rao, S. N., and Reddy, P. R. (2022). Heuristically Modified Fusion-based Hybrid Algorithm for Enhanced Dental Caries Segmentation. In 2022 International Conference on Advances in Computing, Communication and Applied Informatics (ACCAI), pages 1–7.

Mohammad-Rahimi, H., Motamedian, S. R., Rohban, M. H., Krois, J., Uribe, S. E., Mahmoudinia, E., Rokhshad, R., Nadimi, M., and Schwendicke, F. (2022). Deep learning for caries detection: A systematic review. Journal of Dentistry, 122:104115.

Morita, J. c. (2022). 3D Accuitomo 170 |MORITA.

Naebi, M., Saberi, E., Fakour, S. R., Naebi, A., Tabatabaei, S. H., Moghadam, S. A., Bozorgmehr, E., Behnam, N. D., and Azimi, H. (2016). Detection of carious lesions and restorations using particle swarm optimization algorithm. International Journal of Dentistry, 2016:1–7.

Pine, C. M., Harris, R. V., Burnside, G., and Merrett, M. C. W. (2006). An investigation of the relationship between untreated decayed teeth and dental sepsis in 5-year-old children. British Dental Journal, 200(1):45–47. Number: 1 Publisher: Nature Publishing Group.

Pitts, N. B., Zero, D. T., Marsh, P. D., Ekstrand, K., Weintraub, J. A., Ramos-Gomez, F., Tagami, J., Twetman, S., Tsakos, G., and Ismail, A. (2017). Dental caries. Nature Reviews Disease Primers, 3(1):1–16. Number: 1 Publisher: Nature Publishing Group.

Ronneberger, O., Fischer, P., and Brox, T. (2015). U-Net: Convolutional Networks for Biomedical Image Segmentation. arXiv:1505.04597 [cs].

Schwendicke, F., Golla, T., Dreher, M., and Krois, J. (2019). Convolutional neural networks for dental image diagnostics: A scoping review. Journal of Dentistry, 91:103226.

Setzer, F. C., Hinckley, N., Kohli, M. R., and Karabucak, B. (2017). A Survey of Cone-beam Computed Tomographic Use among Endodontic Practitioners in the United States. Journal of Endodontics, 43(5):699–704.

Sklansky, J. (1982). Finding the convex hull of a simple polygon. Pattern Recogn. Lett., 1(2):79–83.

Srivastava, M. M., Kumar, P., Pradhan, L., and Varadarajan, S. (2017). Detection of Tooth caries in Bitewing Radiographs using Deep Learning. arXiv:1711.07312 [cs].

Tsai, D.-M. (1995). A fast thresholding selection procedure for multimodal and unimodal histograms. Pattern Recognition Letters, 16(6):653–666.
Publicado
27/06/2023
ZANINI, Luiz G. K.; NUNES, Fátima L. S.; RUBIRA-BULLEN, Izabel R. F.. Identification and quantification of caries from CBCT segmented images. In: SIMPÓSIO BRASILEIRO DE COMPUTAÇÃO APLICADA À SAÚDE (SBCAS), 23. , 2023, São Paulo/SP. Anais [...]. Porto Alegre: Sociedade Brasileira de Computação, 2023 . p. 1-12. ISSN 2763-8952. DOI: https://doi.org/10.5753/sbcas.2023.229376.