Artificial intelligence in orthodontics

https://doi.org/10.53730/ijhs.v5nS1.5673

Authors

  • Avkash Sakolia PG Student (1st year), Department of Department of orthodontics & dentofacial Orthopaedics, Desh Bhagat Dental College & Hospital, Mandi Gobindgarh, 147301

Keywords:

artificial intelligence, digital orthodontics, machine learning, treatment planning

Abstract

The clinical use of artificial intelligence technology in orthodontics has increased significantly in recent years. Artificial intelligence can be utilized in almost every part of orthodontic workflow. It is an important decision-making aid as well as being a tool for building more efficient treatment methods. The use of artificial intelligence reduces costs, accelerates the diagnosis and treatment process and reduces or even eliminates the need for manpower. The aim of this articleis to discussArtificial intelligence in orthodontic diagnosis, treatment planning, and predicting the prognosis.

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References

Gelfand AE, Hills SE, Racine-Poon A, Smith AF. Illustration of bayesian inference in normal data models using gibbs sampling. J Am Stat Assoc 1990;85:972-85.

Rohrer B. End-to-end Machine Learning Library; 2020.

Croskerry P. The importance of cognitive errors in diagnosis and strategies to minimize them. Acad Med 2003;78:775-80.

Thanathornwong B. Bayesian-based decision support system for assessing the needs for orthodontic treatment. Healthc Inform Res 2018;24:22-8.

Proffit WR. The evolution of orthodontics to a data-based specialty. Am J Orthod Dentofacial Orthop2000;117:545-7.

Hicks EP, Kluemper GT. Heuristic reasoning and cognitive biases: Are they hindrances to judgments and decision making in orthodontics? Am J Orthod dentofacial Orthop2011;139:297-304.

Norman GR, Eva KW. Diagnostic error and clinical reasoning. Med Educ2010;44:94-100.

Einhorn HJ, Hogarth RM. Prediction, diagnosis, and causal thinking in forecasting. In: Behavioral Decision Making. Berlin: Springer; 1985. p. 311-28.

Merrifield LL, Klontz HA, Vaden JL. Differential diagnostic analysis system. Am J Orthod Dentofacial Orthop1994;106:641-8.

Makaremi M, Lacaule C, Mohammad-Djafari A. Deep learning and artificial intelligence for the determination of the cervical vertebra maturation degree from lateral radiography. Entropy 2019;21:1222

Ghahramani Z. Probabilistic machine learning and artificial intelligence. Nature 2015;521:452-9.

Koller D, Friedman N. Probabilistic Graphical Models: Principles and Techniques. Cambridge: MIT Press; 2009.

Jordan MI. An Introduction to Probabilistic Graphical Models, preparation; 2003.

Kim, B.M., Kang, B.Y., Kim, H.G., Baek, S.H. Prognosis prediction for class III malocclusion treatment by feature wrapping method. Angle Orthod.2009; 79, 683-691.

Yagi, M., Ohno, H., Takada, K. Decision-making system for orthodontic treatment planning based on direct implementation of expertise knowledge. 2010 Annu. Int. Conf. IEEE Eng. Med. Biol.2010; 2894-2897.

Auconi, P., Caldarelli, G., Scala, A., Ierardo, G., Polimeni, A. A network approach to orthodontic diagnosis. Orthod. Craniofac. Res. .2011;14, 189-197.

Niño-Sandoval, T.C., Perez, S.V.G., González, F.A., Jaque, R.A., Infante-Contreras, C. An automatic method for skeletal patterns classification using craniomaxillary variables on a Colombian population. Forensic Sci. Int.2016; 261, 159-e1.

Murata, S., Lee, C., Tanikawa, C., Date, S. Towards a fully automated diagnostic system for orthodontic treatment in dentistry. 2017 IEEE 13th Int. Conf. e-Science 2017;1-8.

Wang, X., Cai, B., Cao, Y., Zhou, C., Yang, L., Liu, R., Long, X., Wang, W., Gao, D., Bao, B. Objective method for evaluating orthodontic treatment from the lay perspective: An eye-tracking study. Am. J. Orthod. Dentofac. Orthop. 2016; 150, 601-610.

Schwendicke F, Golla T, Dreher M, Krois J. Convolutional neural networks for dental image diagnostics: A scoping review. J Dent. 2019;91:103226.

Esteva A, Kuprel B, Novoa RA, Ko J, Swetter SM, Blau HM, et al. Dermatologist-level classification of skin cancer with deep neural networks. Nature. 2017;542(7639):115-8.

Saba L, Biswas M, Kuppili V, Cuadrado-Godia E, Suri HS, Edla DR, et al. The present and future of deep learning in radiology. Eur J Radiol. 2019;114:14-24.

Rao GK, Mokhtar N, Iskandar YH, Srinivasa AC, editors. Learning orthodontic cephalometry through augmented reality: A conceptual machine learning validation approach. In: 2018 International Conference on Electrical Engineering and Informatics (ICELTICs). United States: IEEE; 2018.

Miller, R., Dijkman, D., Riolo, M., Moyers, R. Graphic computerization of cephalometric data.1971

Ribarevski R, Vig P, Vig KD, Weyant R, O’Brien K. Consistency of orthodontic extraction decisions. Eur J Orthod1996;18:77e80.

Dunbar AC, Bearn D, McIntyre G. The influence of using digital diagnostic information on orthodontic treatment planning - a pilot study. J HealthcEng2014;5:411e27.

Luke LS, Atchison KA, White SC. Consistency of patient classification in orthodontic diagnosis and treatment planning. Angle Orthod1998;68:513e20.

Xie X, Wang L, Wang A. Artificial neural network modeling for deciding if extractions are necessary prior to orthodontic treatment. Angle Orthod2010;80:262e6.

Jung SK, Kim TW. New approach for the diagnosis of extractions with neural network machine learning. Am J Orthod Dentofacial Orthop2016;149:127e33.

Han S. The fourth industrial revolution and oral and maxillofacial surgery. J Korean Assoc Oral Maxillofac Surg. 2018;44(5):205-6.

Bouletreau P, Makaremi M, Ibrahim B, Louvrier A, Sigaux N. Artificial intelligence: Applications in orthognathic surgery. J Stomatol Oral Maxillofac Surg. 2019;120(4):347-54.

Choi HI, Jung SK, Baek SH, Lim WH, Ahn SJ, Yang IH, et al. Artificial intelligent model with neural network machine learning for the diagnosis of orthognathic surgery. J Craniofac Surg. 2019;30(7):1986-9.

Knoops PGM, Papaioannou A, Borghi A, Breakey RWF, Wilson AT, Jeelani O, et al. A machine learning framework for automated diagnosis and computerassisted planning in plastic and reconstructive surgery. Sci Rep. 2019;9(1):13597.

Hägg, U., Taranger, J. Maturation indicators and the pubertal growth spurt. Am. J. Orthod.1982; 82, 299-309.

Hägg, U., Taranger, J. Menarche and voice change as indicators of the pubertal growth spurt. Acta Odontol. Scand. 1980;38, 179-186.

Lee, H., Tajmir, S., Lee, J., Zissen, M., Yeshiwas, B.A., Alkasab, T.K., Choy, G., Do, S. Fully automated deep learning system for bone age assessment. J. Digit. Imaging. 2017;30, 427-441.

Spampinato, C., Palazzo, S., Giordano, D., Aldinucci, M., Leonardi, R.. Deep learning for automated skeletal bone age assessment in X-ray images. Med. Image Anal.2017; 36, 41-51.

Iglovikov, V.I., Rakhlin, A., Kalinin, A.A., Shvets, A.A.. Paediatric bone age assessment using deep convolutional neural networks. In deep learning in medical image analysis and multimodal learning for clinical decision support. Springer, Quebec,2018; pp. 300-308.

Larson, D.B., Chen, M.C., Lungren, M.P., Halabi, S.S., Stence, N.V, Langlotz, C.P. Performance of a deep-learning neural network model in assessing skeletal maturity on pediatric hand radiographs. Radiology2018;. 287, 313-322.

Published

31-07-2021

How to Cite

Sakolia, A. (2021). Artificial intelligence in orthodontics. International Journal of Health Sciences, 5(S1), 379–386. https://doi.org/10.53730/ijhs.v5nS1.5673

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Peer Review Articles

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