Machine Learning Applications in Dentistry

Objective & Hypothesis:Artificial Intelligence has emerged as a breakthrough in many fields including medicine and dentistry where new approaches can be employed to solve challenging decision making processes faced in the dental field.

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Key Findings & Data:Artificial intelligence can be used as a decision support mechanism to solve the increasing population and consequently the increasing dental treatment needs.

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Conclusion & Impact:This mini-review covers some of the recent studies in this area and envisions future directions on the use of machine learning in dental problems.

🔬Design:Review & Systematic Survey

📍Scope:Comprehensive Sample

⚙️Variables:Diş Hekimliği · Makine Öğrenimi Uygulamaları · Yapay zeka

Abstract

Artificial Intelligence has emerged as a breakthrough in many fields including medicine and dentistry where new approaches can be employed to solve challenging decision making processes faced in the dental field. Artificial intelligence can be used as a decision support mechanism to solve the increasing population and consequently the increasing dental treatment needs. It also assists dentists in diagnosis and treatment planning stages that require expert opinion. This mini-review covers some of the recent studies in this area and envisions future directions on the use of machine learning in dental problems.

Özet

Yapay Zeka, diş hekimliği alanında karşılaşılan zorlu karar verme süreçlerini çözmek için yeni yaklaşımların kullanılabildiği tıp ve diş hekimliği dahil birçok alanda bir atılım olarak ortaya çıkmıştır. Artan nüfus ve buna bağlı olarak artan diş tedavi ihtiyaçlarını çözmek için yapay zeka bir karar destek mekanizması olarak kullanılabilir. Ayrıca uzman görüşü gerektiren teşhis ve tedavi planlama aşamalarında diş hekimlerine yardımcı olur. Bu mini inceleme, bu alandaki son çalışmalardan bazılarını kapsamakta ve diş problemlerinde makine öğreniminin kullanımına ilişkin gelecekteki yönergeleri öngörmektedir.

Keywords

Diş Hekimliği Makine Öğrenimi Uygulamaları Yapay zeka Dentistry Machine Learning Applications

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Cite This Article

SENİRKENTLİ, G. B., BOSTANCI, G. E., GÜZEL, M. S., UNAL, M. (2022). Machine Learning Applications in Dentistry. Selcuk Dental Journal, Cilt: 9 - Sayı: 3, 977–983. https://ajindex.com/articles/863590

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References (1)

Joshi AV. Machine learning and artificial intelligence. Springer; 2020. 2.Park, W. J., & Park, J. B. (2018). History and application of artificial neural networks in dentistry. European journal of dentistry, 12(4), 594. 3.Schwendicke, F. A., Samek, W., & Krois, J. (2020). Artificial intelligence in dentistry: chances and challenges. Journal of dental research, 99(7), 769-774. 4.Shan, T., Tay, F. R., & Gu, L. (2020). Application of Artificial Intelligence in Dentistry. Journal of dental research, 0022034520969115. 5.Wang H, Zhang H. Movie genre preference prediction using machine learning for customer-based information. In: 2018 IEEE 8th Annual Computing and Communication Workshop and Conference (CCWC) IEEE; 2018:110–6. 6.Breiman L. Random forests. Machine Learning 2001;45(1):5–32. 7.Denoeux T. A k-nearest neighbor classification rule based on Dempster-Shafer theory. In: Classic works of the Dempster-Shafer theory of belief functions Springer; 2008:737–60. 8.Ramchoun H, Amine M, Idrissi J, Ghanou Y, Ettaouil M. Multilayer Perceptron: Architecture Optimization and Training. Int. J. Interact. Multimed. Artif. Intell. 2016;4(1):26. MLP. 9.Kim, E. Y., Lim, K. O., & Rhee, H. S. (2009). Predictive modeling of dental pain using neural network. Studies in health technology and informatics, 146, 745-746. 10.Jung, S. K., & Kim, T. W. (2016). New approach for the diagnosis of extractions with neural network machine learning. American Journal of Orthodontics and Dentofacial Orthopedics, 149(1), 127-133. 11. Nilsson NJ. Artificial intelligence: a new synthesis. Morgan Kaufmann Publishers Inc, 1998:513. 12.Mario MC, Abe JM, Ortega NR, Del Santo M Jr. Paraconsistent artificial neural network as auxiliary in cephalometric diagnosis. Artif Organs. 2010;34(7):E215–221. doi:10.1111/j.1525-1594.2010.00994.x. 13.Ozden FO, Özgönenel O, Özden B, Aydogdu A, Niger J. Diagnosis of periodontal diseases using different classification algorithms: A preliminary study. Clin Pract. 2015;18(3):416–421. doi:10.4103/1119- 3077.151785. 14.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 Oct;77:106-111. doi: 10.1016/j.jdent.2018.07.015. Epub 2018 Jul 26. PMID: 30056118. 15.Brignardello-Petersen R. Artificial intelligence system seems to be able to detect a high proportion of periapical lesions in cone-beam computed tomographic images. J Am Dent Assoc. 2020 Sep;151(9):e83. doi: 10.1016