Imaging Sci Dent.  2019 Mar;49(1):1-7. 10.5624/isd.2019.49.1.1.

An overview of deep learning in the field of dentistry

Affiliations
  • 1Department of Oral and Maxillofacial Radiology, School of Dentistry, Pusan National University, Dental Research Institute, Yangsan, Korea.
  • 2Department of Oral and Maxillofacial Radiology and Dental Research Institute, School of Dentistry, Seoul National University, Seoul, Korea. hmslsh@snu.ac.kr

Abstract

PURPOSE
Artificial intelligence (AI), represented by deep learning, can be used for real-life problems and is applied across all sectors of society including medical and dental field. The purpose of this study is to review articles about deep learning that were applied to the field of oral and maxillofacial radiology.
MATERIALS AND METHODS
A systematic review was performed using Pubmed, Scopus, and IEEE explore databases to identify articles using deep learning in English literature. The variables from 25 articles included network architecture, number of training data, evaluation result, pros and cons, study object and imaging modality.
RESULTS
Convolutional Neural network (CNN) was used as a main network component. The number of published paper and training datasets tended to increase, dealing with various field of dentistry.
CONCLUSION
Dental public datasets need to be constructed and data standardization is necessary for clinical application of deep learning in dental field.

Keyword

Artificial Intelligence; Deep Learning; Dentistry; Radiology

MeSH Terms

Artificial Intelligence
Dataset
Dentistry*
Learning*

Figure

  • Fig. 1 Number of articles from 2016 to 2018.

  • Fig. 2 Median size of training datasets from 2016 to 2018.


Cited by  3 articles

Evaluation of VGG-16 deep learning algorithm for dental caries classification
Min-Ji Byon, Eun-Joo Jun, Ji-Soo Kim, Jae-Joon Hwang, Seung-Hwa Jeong
J Korean Acad Oral Health. 2021;45(4):227-232.    doi: 10.11149/jkaoh.2021.45.4.227.

Deep learning algorithms for identifying 79 dental implant types
Hyun-Jun Kong, Jin-Yong Yoo, Sang-Ho Eom, Jun-Hyeok Lee
J Dent Rehabil Appl Sci. 2022;38(4):196-203.    doi: 10.14368/jdras.2022.38.4.196.

Classification of dental implant systems using cloud-based deep learning algorithm: an experimental study
Hyun Jun Kong
J Yeungnam Med Sci. 2023;40(Suppl):S29-S36.    doi: 10.12701/jyms.2023.00465.


Reference

1. Park WJ, Park JB. History and application of artificial neural networks in dentistry. Eur J Dent. 2018; 12:594–601.
Article
2. Mupparapu M, Wu CW, Chen YC. Artificial intelligence, machine learning, neural networks, and deep learning: futuristic concepts for new dental diagnosis. Quintessence Int. 2018; 49:687–688.
3. Burt JR, Torosdagli N, Khosravan N, RaviPrakash H, Mortazi A, Tissavirasingham F, et al. Deep learning beyond cats and dogs: recent advances in diagnosing breast cancer with deep neural networks. Br J Radiol. 2018; 91:20170545.
Article
4. Rabuñal JR, Dorado J. Artificial neural networks in real-life applications. IGI Global: Hershey;2005. p. 166–346.
5. Panchal G, Ganatra A, Kosta YP, Panchal D. Behaviour analysis of multilayer perceptrons with multiple hidden neurons and hidden layers. Int J Comput Theory Eng. 2011; 3:332–337.
6. Imangaliyev S, van der Veen MH, Volgenant CM, Keijser BJ, Crielaard W, Levin E. Deep learning for classification of dental plaque images. In : In : Conca PP, Nicosia GG, editors. Machine learning, optimization, and Big data. Second International Workshop, MOD 2016; August 26-29, 2016; Volterra, Italy. Heidelberg: Springer;2016. p. 407–410. Revised Selected Papers.
7. Eun H, Kim C. Oriented tooth localization for periapical dental X-ray images via convolutional neural network. In : 2016 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA 2016); 2016 Dec 13-16; Jeju, Korea. Red Hook, NY: IEEE;p. 33–39.
8. De Tobel J, Radesh P, Vandermeulen D, Thevissen PW. An automated technique to stage lower third molar development on panoramic radiographs for age estimation: a pilot study. J Forensic Odontostomatol. 2017; 2:42–54.
9. Rana A, Yauney G, Wong LC, Gupta O, Muftu A, Shah P. Automated segmentation of gingival diseases from oral images. In : IEEE-NIH 2017 Special Topics Conference on Healthcare Innovations and Point-of-Care Technologies; Bethesda, MD; National Institutes of Health. p. 144–147.
10. Lee H, Park M, Kim J. Cephalometric landmark detection in dental X-ray images using convolutional neural networks. In : Proc. SPIE 10134, Medical Imaging 2017: Computer-Aided Diagnosis, 101341W; 3 March 2017; Available from: https://doi.org/10.1117/12.2255870.
Article
11. Prajapati SA, Nagaraj R, Mitra S. Classification of dental diseases using CNN and transfer learning. In : 2017 5th International Symposium on Computational and Business Intelligence (ISCBI); Dubai. 2017. p. 70–74. Available from: https://doi.org/10.1109/ISCBI.2017.8053547.
Article
12. Imangaliyev S, van der Veen MH, Volgenant CM, Loos BG, Keijser BJ, Crielaard W, et al. Classification of quantitative light-induced fluorescence images using convolutional neural network. arXiv:1705.09193. 2017. cited 2018 November 20. Available from: https://arxiv.org/pdf/1705.09193.pdf.
13. Miki Y, Muramatsu C, Hayashi T, Zhou X, Hara T, Katsumata A, et al. Classification of teeth in cone-beam CT using deep convolutional neural network. Comput Biol Med. 2017; 80:24–29.
Article
14. Yauney G, Angelino K, Edlund DA, Shah P. Convolutional neural network for combined classification of fluorescent biomarkers and expert annotations using white light images. In : 2017 IEEE 17th International Conference on Bioinformatics and Bioengineering (BIBE); Washington, DC; 2017. p. 303–309. Available from: https://doi.org/10.1109/BIBE.2017.00-37.
Article
15. Oktay AB. Tooth detection with Convolutional Neural Networks. Trabzon: 2017 Medical Technologies National Congress (TIPTEKNO);2017. p. 1–4. Available from: https://doi.org/10.1109/TIPTEKNO.2017.8238075.
Article
16. Miki Y, Muramatsu C, Hayashi T, Zhou X, Hara T, Katsumata A, et al. In : Armato SG, Petrick NA, editors. Tooth labeling in cone-beam CT using deep convolutional neural network for forensic identification. Medical Imaging 2017: Computer-Aided Diagnosis.Proceedings Volume 10134, SPIE Medical Imaging; 2017 Feb 11-16; Orlando, USA: SPIE;2017. Available from: https://doi.org/10.1117/12.2254332.
Article
17. Murata S, Lee C, Tanikawa C, Date S. Towards a fully automated diagnostic system for orthodontic treatment in dentistry. In : 2017 IEEE 13th International Conference on e-Science (e-Science); Auckland. 2017. p. 1–8. Available from: https://doi.org/10.1109/eScience.2017.12.
Article
18. Xu X, Liu C, Zheng Y. 3D tooth segmentation and labeling using deep convolutional neural networks. IEEE Trans Vis Comput Graph. (in press).
Article
19. Du X, Chen Y, Zhao J, Xi Y. A convolutional neural network based auto-positioning method for dental arch in rotational panoramic radiography. Conf Proc IEEE Eng Med Biol Soc. 2018; 2018:2615–2618.
Article
20. Zhang K, Wu J, Chen H, Lyu P. An effective teeth recognition method using label tree with cascade network structure. Comput Med Imaging Graph. 2018; 68:61–70.
Article
21. Yang J, Xie Y, Liu L, Xia B, Cao Z, Guo C. Automated dental image analysis by deep learning on small dataset. In : 2018 IEEE 42nd Annual Computer Software and Applications Conference (COMPSAC); Tokyo. 2018. p. 492–497. Available from: https://doi.org/10.1109/COMPSAC.2018.00076.
Article
22. Andreas W, Sudesh G, Stefan W. Automatic teeth segmentation in panoramic X-ray images using a coupled shape model in combination with a neural network. In : Proceedings from the 21st International Conference on Medical Image Computing and Computer Assisted Intervention, Part IV; 2018 Sep 16-20; Granada, Spain. Basel: Springer;2018. Available from: https://doi.org/10.1007/978-3-030-00937-3_81.
Article
23. Torosdagli N, Liberton DK, Verma P, Sincan M, Lee JS, Bagci U. Deep geodesic learning for segmentation and anatomical landmarking. IEEE Trans Med Imaging. (in press).
Article
24. Karimian N, Salehi HS, Mahdian M, Alnajjar H, Tadinada A. Deep learning classifier with optical coherence tomography images for early dental caries detection. In : Proc. SPIE 10473, Lasers in Dentistry XXIV, 1047304; 8 February 2018; Available from: https://doi.org/10.1117/12.2291088.
Article
25. Hatvani J, Horváth A, Michetti J, Basarab A, Kouamé D, Gyöngy M. Deep learning-based super-resolution applied to dental computed tomography. IEEE Transactions on Radiation and Plasma Medical Sciences. Available from: https://doi.org/10.1109/TRPMS.2018.2827239.
Article
26. Lee JH, Kim DH, Jeong SN, Choi SH. Detection and diagnosis of dental caries using a deep learning-based convolutional neural network algorithm. J Dent. 2018; 77:106–111.
Article
27. Lee JH, Kim DH, Jeong SN, Choi SH. Diagnosis and prediction of periodontally compromised teeth using a deep learning-based convolutional neural network algorithm. J Periodontal Implant Sci. 2018; 48:114–123.
Article
28. Egger J, Pfarrkirchner B, Gsaxner C, Lindner L, Schmalstieg D, Wallner J. Fully convolutional mandible segmentation on a valid ground - truth dataset. Conf Proc IEEE Eng Med Biol Soc. 2018; 2018:656–660.
29. Lee JS, Adhikari S, Liu L, Jeong HG, Kim H, Yoon SJ. Osteoporosis detection in panoramic radiographs using a deep convolutional neural network-based computer-assisted diagnosis system: a preliminary study. Dentomaxillofac Radiol. 2018; 20170344. (in press).
Article
30. Chu P, Bo C, Liang X, Yang J, Megalooikonomou V, Yang F, et al. Using octuplet siamese network for osteoporosis analysis on dental panoramic radiographs. Conf Proc IEEE Eng Med Biol Soc. 2018; 2018:2579–2582.
Article
31. Kim YD, Jang TW, Han BY, Choi , SJ . Learning to select pretrained deep representations with bayesian evidence framework. In : 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR); Las Vegas, NV. 2016. p. 5318–5326. Available from: https://doi.org/10.1109/CVPR.2016.574.
Article
32. Cheng JZ, Ni D, Chou YH, Qin J, Tiu CM, Chang YC, et al. Computer-aided diagnosis with deep learning architecture: applications to breast lesions in US images and pulmonary nodules in CT scans. Sci Rep. 2016; 6:24454.
Article
33. Suzuki K. Overview of deep learning in medical imaging. Radiol Phys Technol. 2017; 10:257–273.
Article
34. Greenspan H, Ginneken BV, Summers RM. Guest editorial: deep learning in medical imaging: overview and future promise of an exciting new technique. IEEE Trans Med Imaging. 2016; 35:1153–1159.
Article
35. Berman JJ. Confidentiality issues for medical data miners. Artif Intell Med. 2002; 26:25–36.
Article
36. Cooper T, Collman J. Managing information security and privacy in healthcare data mining. In : Chen H, Fuller SS, Friedman CH, editors. Medical informatics: knowledge management and data mining in biomedicine. New York: Springer;2005. p. 95–137.
37. Weese J, Lorenz C. Four challenges in medical image analysis from an industrial perspective. Med Image Anal. 2016; 33:44–49.
Article
38. Cho J, Lee K, Shin E, Choy G, Do S. How much data is needed to train a medical image deep learning system to achieve necessary high accuracy? arXiv:1511.06348 [Preprint]. 2016. cited 2018 Nov 20. Available from: https://arxiv.org/abs/1511.06348.
39. Pauwels R, Araki K, Siewerdsen JH, Thongvigitmanee SS. Technical aspects of dental CBCT: state of the art. Dentomaxillofac Radiol. 2015; 44:20140224.
Article
40. Devlin H, Yuan J. Object position and image magnification in dental panoramic radiography: a theoretical analysis. Dentomaxillofac Radiol. 2013; 42:29951683.
Article
41. de Las Heras Gala H, Torresin A, Dasu A, Rampado O, Delis H, Hernández Girón I, et al. Quality control in cone-beam computed tomography (CBCT) EFOMP-ESTRO-IAEA protocol (summary report). Phys Med. 2017; 39:67–72.
Article
42. Chen Y, Elenee Argentinis JD, Weber G. IBM Watson: how cognitive computing can be applied to big data challenges in life sciences research. Clin Ther. 2016; 38:688–701.
Article
43. Anifowose FA. Artificial intelligence application in reservoir characterization and modeling: whitening the black Box. In : Proceedings of the SPE Saudi Arabia section Young Professionals Technical Symposium; 2011 Mar 14-16; Dharan, Saudi Arabia. Dharan: Society of Petroleum Engineers;2011. Available from: https://doi.org/10.2118/155413-MS.v.
Article
Full Text Links
  • ISD
Actions
Cited
CITED
export Copy
Close
Share
  • Twitter
  • Facebook
Similar articles
Copyright © 2024 by Korean Association of Medical Journal Editors. All rights reserved.     E-mail: koreamed@kamje.or.kr