In this article, studies in which AI models such as machine learning, convolutional neural network have found research and usage areas on the basis of different branches of dentistry are reviewed.
📌 3 Key Takeaways
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Objective & Hypothesis:One of the most important actors in the digitization process of our age has been the applications of artificial intelligence (AI).
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Key Findings & Data:While the weak and strong AI sub-concepts and the different AI models within them are being utilized in many fields such as education, industry and medicine today, the interest of the dentistry field, which has started its integration into the digital world with CAD/CAM technology, in AI is increasing day by day.
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Conclusion & Impact:In this article, studies in which AI models such as machine learning, convolutional neural network have found research and usage areas on the basis of different branches of dentistry are reviewed.
🔬Design:Review & Systematic Survey
📍Scope:Comprehensive Sample
⚙️Variables:artificial intelligence (AI) · deep learning · dentistry
Abstract
One of the most important actors in the digitization process of our age has been the applications of artificial intelligence (AI). While the weak and strong AI sub-concepts and the different AI models within them are being utilized in many fields such as education, industry and medicine today, the interest of the dentistry field, which has started its integration into the digital world with CAD/CAM technology, in AI is increasing day by day. In different branches of dentistry; AI provides services to clinicians and researchers in many fields such as disease diagnosis, evaluation of the occurrence or recurrence of diseases such as oral cancer, and prediction of success in surgical and prosthetic treatments. In this article, studies in which AI models such as machine learning, convolutional neural network have found research and usage areas on the basis of different branches of dentistry are reviewed.
Alabi, R.O., Elmusrati, M., Sawazaki-Calone, I., Kowalski, L.P., Haglund, C., Coletta, R.D., Mäkitie, A., Salo, T., Leivo, I., Almangush, A., 2019. Machine learning application for prediction of locoregional recurrences in early oral tongue cancer: a Web-based prognostic tool. Virchows Arch. Oct;475(4):489-497. doi: 10.1007/s00428-019-02642-5
Aliaga, I.J., Vera, V., De Paz, J.F., García, A.E., Mohamad, M.S., 2015. Modelling the longevity of dental restorations by means of a CBR system. Biomed Res Int. 540306. doi:10.1155/2015/540306 Baliga, M.S., 2019. Artificial intelligence - The next frontier in pediatric dentistry. J Indian Soc Pedod Prev Dent. 37:315. Bas, B., Özgönenel, O., Özden, B., Bekçioğlu, B., Bulut, E., Kurt, M., 2012. Use of arti- ficial neural network in differentiation of subgroups of temporoman- dibular internal derangements: A preliminary study. J Oral Maxillofac Surg. 70(1):51–59. doi:10.1016/j.joms.2011.03.069 Borza, D., Darabant, A., Danescu, R., 2018 Automatic Skin Tone Extraction for Visagism Applications. DOI: 10.5220/0006711104660473 In Proceedings of the 13th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2018) - Volume 4: VISAPP, pages 466-473
Burt, J.R., Torosdagli, N., Khosravan, N., RaviPrakash, H., Mortazi, A., Tissavirasingham, F. et al., 2018. Deep learning beyond cats and dogs: recent advances in diagnosing breast cancer with deep neural networks. Br J Radiol. 91: 20170545. Chen, Q., Wu, J., Li, S., Lyu, P., Wang, Y., Li, M., 2016. An ontology-driven, case- based clinical decision support model for removable partial den- ture design. Sci Rep. 6:27855. doi:10.1038/srep27855 Cheng, C., Cheng, X., Dai, N., Jiang, X., Sun, Y., Li, W., 2015. Prediction of facial deformation after complete denture prosthesis using BP neural network. Comput Biol Med. Nov 1;66:103-12. doi: 10.1016/j.compbiomed.2015.08.018. Cho, J., Lee, K., Shin, E., Choy, G., Do, S., 2016. How much data is needed to train a medical image deep learning system to achieve necessary high accuracy? arXiv:1511.06348 [Preprint]. [cited 2018 Nov 20] Available from: https