The Developing Technology of Artificial Intelligence in Endodontics: A Literature Review

Abstract

Artificial intelligence (AI) is a term that interprets technologies that can perform cognitive functions emulating human intelligence. It works by help of the software to learn automatically from patterns or features in the data. It is a popular field of study that contains many theories, methods and technologies, as much as the following major subfields in healthcare and medicine. Use of AI is also popular in many fields of dentistry. The main use in dentistry is in dental education to simulate clinical work on patients and to minimize all the hazards associated with training on a live patient. In dentistry, the use of the deep learning algorithm has been investigated in cases such as the detection of dental caries, periapical lesions, temporomandibular joint problems, and skeletal classifications, and it has been stated that Convolutional Neural Networks (CNN) is a useful aid for diagnosis and treatment planning. This review article was focused on the use of AI in Endodontics such as detection of periapical lesions, prediction of treatment and retreatment methods, detection of root fractures, determination of working length, and evaluation of root canal system morphology and anatomy.

Keywords

• Artificial Intelligence
• Deep Learning
• Machine Learning.

References

1. 1. Umer F, Habib S. Critical Analysis of Artificial Intelligence in Endodontics: A Scoping Review. J Endod. 2022;48(2):152-60.
2. 2. Sherwood AA, Sherwood AI, Setzer FC, K SD, Shamili JV, John C, et al. A Deep Learning Approach to Segment and Classify C-Shaped Canal Morphologies in Mandibular Second Molars Using Cone-beam Computed Tomography. J Endod. 2021;47(12):1907-16.
3. 3. Ergun G, Ataol A, Tekli B. Robotic Applications in Dentistry: A Literature Review. Journal of Ege University School of Dentistry. 2018;39:125-33.
4. 4. Shan T, Tay FR, Gu L. Application of Artificial Intelligence in Dentistry. J Dent Res. 2021;100(3):232-44.
5. 5. Lecun Y, Bengio Y, Hinton G. Deep learning. Nature. 2015;521(7553):436-44.
6. 6. Chinnamgari SK. R Machine Learning Projects: Implement supervised, unsupervised, and reinforcement learning techniques using R 3.5: Packt Publishing Ltd; 2019.
7. 7. Hamet P, Tremblay J. Artificial intelligence in medicine. Metabolism. 2017;69:S36-S40.
8. 8. Agrawal P, Nikhade P. Artificial Intelligence in Dentistry: Past, Present, and Future. Cureus. 2022;14(7):e27405.

9. 9. Moidu NP, Sharma S, Chawla A, Kumar V, Logani A. Deep learning for categorization of endodontic lesion based on radiographic periapical index scoring system. Clin Oral Investig. 2022;26(1):651-8.
10. 10. Cotti E, Schirru E. Present status and future directions: Imaging techniques for the detection of periapical lesions. Int Endod J. 2022;55 Suppl 4:1085-99.
11. 11. Aminoshariae A, Kulild J, Nagendrababu V. Artificial Intelligence in Endodontics: Current Applications and Future Directions. J Endod. 2021;47(9):1352-7.
12. 12. Antony DP, Thomas T, Nivedhitha MS. Two-dimensional Periapical, Panoramic Radiography Versus Three-dimensional Cone-beam Computed Tomography in the Detection of Periapical Lesion After Endodontic Treatment: A Systematic Review. Cureus. 2020;12(4):e7736.
13. 13. Setzer FC, Shi KJ, Zhang Z, Yan H, Yoon H, Mupparapu M, et al. Artificial Intelligence for the Computer-aided Detection of Periapical Lesions in Cone-beam Computed Tomographic Images. J Endod. 2020;46(7):987-93.
14. 14. Orhan K, Bayrakdar IS, Ezhov M, Kravtsov A, Ozyurek T. Evaluation of artificial intelligence for detecting periapical pathosis on cone-beam computed tomography scans. Int Endod J. 2020;53(5):680-9.
15. 15. Pauwels R, Brasil DM, Yamasaki MC, Jacobs R, Bosmans H, Freitas DQ, et al. Artificial intelligence for detection of periapical lesions on intraoral radiographs: Comparison between convolutional neural networks and human observers. Oral Surg Oral Med Oral Pathol Oral Radiol. 2021;131(5):610-6.
16. 16. Hiraiwa T, Ariji Y, Fukuda M, Kise Y, Nakata K, Katsumata A, et al. A deep-learning artificial intelligence system for assessment of root morphology of the mandibular first molar on panoramic radiography. Dentomaxillofacial Radiology. 2019;48(3).
17. 17. Lahoud P, EzEldeen M, Beznik T, Willems H, Leite A, Van Gerven A, et al. Artificial Intelligence for Fast and Accurate 3-Dimensional Tooth Segmentation on Cone-beam Computed Tomography. J Endod. 2021;47(5):827-35.
18. 18. Jeon SJ, Yun JP, Yeom HG, Shin WS, Lee JH, Jeong SH, et al. Deep-learning for predicting C-shaped canals in mandibular second molars on panoramic radiographs. Dentomaxillofac Radiol. 2021;50(5):20200513.
19. 19. Saghiri MA, Asgar K, Boukani KK, Lotfi M, Aghili H, Delvarani A, et al. A new approach for locating the minor apical foramen using an artificial neural network. Int Endod J. 2012;45(3):257-65.
20. 20. Liao WC, Chen CH, Pan YH, Chang MC, Jeng JH. Vertical Root Fracture in Non-Endodontically and Endodontically Treated Teeth: Current Understanding and Future Challenge. J Pers Med. 2021;11(12).
21. 21. Yoshino K, Ito K, Kuroda M, Sugihara N. Prevalence of vertical root fracture as the reason for tooth extraction in dental clinics. Clin Oral Investig. 2015;19(6):1405-9.
22. 22. Patel S, Bhuva B, Bose R. Present status and future directions: vertical root fractures in root filled teeth. Int Endod J. 2022;55 Suppl 3(Suppl 3):804-26.
23. 23. Fukuda M, Inamoto K, Shibata N, Ariji Y, Yanashita Y, Kutsuna S, et al. Evaluation of an artificial intelligence system for detecting vertical root fracture on panoramic radiography. Oral Radiol. 2020;36(4):337-43.
24. 24. Shah H, Hernandez P, Budin F, Chittajallu D, Vimort JB, Walters R, et al. Automatic quantification framework to detect cracks in teeth. Proc SPIE Int Soc Opt Eng. 2018;10578.
25. 25. Campo L, Aliaga IJ, De Paz JF, Garcia AE, Bajo J, Villarubia G, et al. Retreatment Predictions in Odontology by means of CBR Systems. Comput Intell Neurosci. 2016;2016:7485250.
26. 26. Schwendicke F, Samek W, Krois J. Artificial Intelligence in Dentistry: Chances and Challenges. J Dent Res. 2020;99(7):769-74.