Abstract
The purpose of this study is to investigate the gradual incorporation of deep learning in the dental healthcare system, offering an easy and efficient diagnosis. For that, an electronic search was conducted in the Institute of Electrical and Electronics Engineers (IEEE) Xplore, ScienceDirect, Journal of Dentistry, Health Informatics Journal, and other credible resources. The studies varied with their tools and techniques used for the diagnosis while coping with the rapid deep-learning evolving base, with different types of conducting tools and analysis for the data. An inclusion criterion was set to specify the quality of the chosen papers. The papers included provided information about the neural network models used like model type, the targeted disease, and results evaluating parameters. The referenced databases ranged from 88 to 12600 clinical images. All the included studies used different neural network models with different outcome metrics. This inconsistency of the methods used makes them incomparable which makes reaching a reliable conclusion more complicated. The paper voiced some observations about the methods used with future recommendations. The goal is to review the deep learning methods that can be used in medical diagnosis.
Nile University.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Park, W.J., Park, J.-B.: History and application of artificial neural networks in dentistry. Eur. J. Dent. 12(04), 594–601 (2018)
El Tantawi, M., Aly, N.M., Attia, D., Abdelrahman, H., Mehaina, M.: Dentist availability in Egypt: a 20-year study of supply, potential demand and economic factors. Eastern Mediterr. Health J. 26(9), 1078–1086 (2020)
Kim, D., Choi, J., Ahn, S., Park, E.: A smart home dental care system: integration of deep learning, image sensors, and mobile controller. J. Ambient Intell. Humanized Comput. 1–9 (2021). https://doi.org/10.1007/s12652-021-03366-8
Kohara, E.K., Abdala, C.G., Novaes, T.F., Braga, M.M., Haddad, A.E., Mendes, F.M.: Is it feasible to use smartphone images to perform telediagnosis of different stages of occlusal caries lesions? PLoS ONE 13(9), e0202116 (2018)
Quintero-Rojas, J., González, J.D.: Use of convolutional neural networks in smartphones for the identification of oral diseases using a small dataset. Revista Facultad de Ingenierà 30(55), e11846 (2021)
Lin, H., Chen, H., Weng, L., Shao, J., Lin, J.: Automatic detection of oral cancer in smartphone-based images using deep learning for early diagnosis. J. Biomed. Opt. 26(8), 086007 (2021)
Duong, D.L., Kabir, M.H., Kuo, R.F.: Automated caries detection with smartphone color photography using machine learning. Health Inform. J. 27(2) (2021)
Zhang, X., et al.: Development and evaluation of deep learning for screening dental caries from oral photographs. Oral Dis. 28(1), 173–181 (2020)
Liang, Y., et al.: Proceedings of the 2020 CHI Conference on Human Factors in Computing System. Association for Computing Machinery, New York (2020)
Lee, J.-H., Kim, D.-H., Jeong, S.-N., Choi, S.-H.: Detection and diagnosis of dental caries using a deep learning-based convolutional neural network algorithm. J. Dent. 77, 106–111 (2018)
Krois, J., et al.: Deep learning for the radiographic detection of periodontal bone loss. Sci. Rep. 9(1), 1–6 (2019)
Cantu, A.G., et al.: Detecting caries lesions of different radiographic extension on bitewings using deep learning. J. Dent. 100, 103–425 (2020)
Jeyaraj, P.R., Samuel Nadar, E.R.: Computer-assisted medical image classification for early diagnosis of oral cancer employing deep learning algorithm. J. Cancer Res. Clin. Oncol. 145(4), 829–837 (2019). https://doi.org/10.1007/s00432-018-02834-7
Liu, L., Xu, J., Huan, Y., Zou, Z., Yeh, S.-C., Zheng, L.-R.: A smart dental health-IoT platform based on intelligent hardware, deep learning, and mobile terminal. IEEE J. Biomed. Health Inform. 24(3), 1–7 (2021)
Takahashi, T., Nozaki, K., Gonda, T., Mameno, T., Ikebe, K.: Deep learning-based detection of dental prostheses and restorations. Sci. Rep. 11(1), 99–110 (2016)
Lee, S., Oh, S., Jo, J., Kang, S., Shin, Y., Park, J.: Deep learning for early dental caries detection in bitewing radiographs. Sci. Rep. 11(1), 1–8 (2021)
You, W., Hao, A., Li, S., Wang, Y., Xia, B.: Deep learning-based dental plaque detection on primary teeth: a comparison with clinical assessments. BMC Oral Health 20, 1–7 (2020). https://doi.org/10.1186/s12903-020-01114-6
Kim, J., Lee, H.-S., Song, I.-S., Jung, K.-H.: DeNTNet: Deep Neural Transfer Network for the detection of periodontal bone loss using panoramic dental radiographs. Sci. Rep. 9(1), 1–9 (2019)
Murata, M., et al.: Deep-learning classification using convolutional neural network for evaluation of maxillary sinusitis on panoramic radiography. Oral Radiol. 35(3), 301–307 (2019). https://doi.org/10.1007/s11282-018-0363-7
Srivastava, M.M., Kumar, P., Pradhan, L., Varadarajan, S.: Detection of tooth caries in bitewing radiographs using deep learning. arXiv preprint arXiv:1711.07312 (2017)
Godlevsky, L., et al.: Application of mobile photography with smartphone cameras for monitoring of early caries appearance in the course of orthodontic correction with dental brackets. Appl. Med. Inform. 33(4), 21–26 (2013)
Ezhov, M., et al.: Clinically applicable artificial intelligence system for dental diagnosis with CBCT. Sci. Rep. 11(1), 1–16 (2021)
Casalegno, F., et al.: Caries detection with near-infrared transillumination using deep learning. J. Dent. Res. 98(11), 1227–1233 (2019)
Yang, H., et al.: Deep learning for automated detection of cyst and tumors of the jaw in panoramic radiographs. J. Clin. Med. 9(6), 18–39 (2020)
Al Kheraif, A.A., Wahba, A.A., Fouad, H.: Detection of dental diseases from radiographic 2D dental image using hybrid graph-cut technique and convolutional neural network. Measurement 146, 333–342 (2019)
Lian, L., Zhu, T., Zhu, F., Zhu, H.: Deep learning for caries detection and classification. Diagnostics 11(9), 1672 (2021)
Ghaedi, L., et al.: An automated dental caries detection and scoring system for optical images of tooth occlusal surface. Journal 2(5), 99–110 (2016)
Berdouses, E.D., Koutsouri, G.D., Tripoliti, E.E., Matsopoulos, G.K., Oulis, C.J., Fotiadis, D.I.: A computer-aided automated methodology for the detection and classification of occlusal caries from photographic color images. Comput. Biol. Med. 62, 119–135 (2015)
Askar, H., et al.: Detecting white spot lesions on dental photography using deep learning: a pilot study. J. Dent. 107, 103615 (2021)
Yang, J., Xie, Y., Liu, L., Xia, B., Cao, Z., Guo, C.: 2018 IEEE 42nd Annual Computer Software and Applications Conference (COMPSAC), vol. 1, pp. 492–497. IEEE (2018)
Chen, Y., Argentinis, J.D.E., Weber, G.: IBM Watson: how cognitive computing can be applied to big data challenges in life sciences research. Clin. Ther. 38(4), 688–701 (2016)
Alenezi, N.S.A.L.K.: A method of skin disease detection using image processing and machine learning. Procedia Comput. Sci. 163, 85–92 (2019)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2022 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
Elsayed, A., Mostafa, H., Tarek, R., Mohamed, K., Hossam, A., Selim, S. (2022). Oral Dental Diagnosis Using Deep Learning Techniques: A Review. In: Yang, G., Aviles-Rivero, A., Roberts, M., Schönlieb, CB. (eds) Medical Image Understanding and Analysis. MIUA 2022. Lecture Notes in Computer Science, vol 13413. Springer, Cham. https://doi.org/10.1007/978-3-031-12053-4_60
Download citation
DOI: https://doi.org/10.1007/978-3-031-12053-4_60
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-12052-7
Online ISBN: 978-3-031-12053-4
eBook Packages: Computer ScienceComputer Science (R0)