Artificial Intelligence in Orthodontics: Prediction and Planning



  • Vinoth Kumar. R

    Thai Moogambigai Dental College and Hospital

  • Abirami Vetriselvan

    Thai Moogambigai Dental College and Hospital

  • Magdline A

  • Rajakumar P

    Thai Moogambigai Dental College and Hospital

  • M.K.Karthikeyan


Artificial Intelligence, Automated diagnosis, Customized treatment, Clinical Decision Support Systems, Neural Network, Virtual systems


Artificial intelligence (AI) with advancement of technology has experienced remarkable growth and development in the field of dentistry. AI is an excellent tool that performs several tasks from diagnosis, treatment planning, predicting the outcome and prognosis particularly in the field of orthodontics based on individual preferences and constructed algorithm models. The present review was carried out to discuss briefly on the role and impact of AI in the field of orthodontics. It was observed that most of the AI models are based on artificial neural networks (ANNs) and convolutional neural networks (CNNs) systems widely used for Cephalometric landmarks identification, image recognition, decision making system to assist treatment planning, prediction of need for extraction and/or orthognathic surgeries, evaluating the cervical vertebrae growth pattern and maturation, predicting the facial attractiveness and post-orthognathic surgery facial profile. Further research on application of AI should be carried out focusing on formulating and establishing cloud-based platforms, integrating large data to improve learning algorithms and construct advanced automated decision making model systems with high specificity, precision, reliability to predict exact outcomes within a short span of time and improve the quality of life.


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Schwendicke FA, Samek W, Krois J. Artificial intelligence in dentistry: chances and challenges. Journal of dental research. 2020 Jul;99(7):769-74.

Mintz Y, Brodie R. Introduction to artificial intelligence in medicine. Minimally Invasive Therapy & Allied Technologies. 2019 Mar 4;28(2):73-81.

Tandon D, Rajawat J. Present and future of artificial intelligence in dentistry. Journal of Oral Biology and Craniofacial Research. 2020 Jul 24.

Chen YW, Stanley K, Att W. Artificial intelligence in dentistry: Current applications and future perspectives. Quintessence Int. 2020 Mar 1;51(3):248-57.

de Cos, Francisco Javier. “artificial intelligence and machine learning applications in Health Sciences.” (2019): 801-802.

Moghimi S, Talebi M, Parisay I. Design and implementation of a hybrid genetic algorithm and artificial neural network system for predicting the sizes of unerupted canines and premolars. Eur J Orthod. 2012; 34(4):480–486.

Machoy ME, Szyszka-Sommerfeld L, Vegh A, Gedrange T, Woźniak K. The ways of using machine learning in dentistry. Advances in clinical and experimental medicine: official organ Wroclaw Medical University. 2020 Mar 1;29(3):375-84.

Hwang JJ, Jung YH, Cho BH, Heo MS. An overview of deep learning in the field of dentistry. Imaging science in dentistry. 2019 Mar 1;49(1):1-7.

Kang, Dae-Young, Hieu Pham Duong, and Jung-Chul Park. “Application of deep learning in dentistry and implantology.” (2020): 148-181.

Lee JH, Kim DH, Jeong SN, Choi SH. Detection and diagnosis of dental caries using a deep learning-based convolutional neural network algorithm. Journal of dentistry. 2018 Oct 1;77:106-11.

Corbella S, Srinivas S, Cabitza F. Applications of deep learning in dentistry. Oral Surgery, Oral Medicine, Oral Pathology and Oral Radiology. 2020 Nov 18.

Shan T, Tay FR, Gu L. Application of artificial intelligence in dentistry. Journal of Dental Research. 2021 Mar;100(3):232-44.

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.

Arık SÖ, Ibragimov B, Xing L. Fully automated quantitative cephalometry using convolutional neural networks. J Med Imaging (Bellingham). 2017; 4(1):014501.

Park JH, Hwang HW, Moon JH, Yu Y, Kim H, Her SB, et al. Automated identification of cephalometric landmarks: Part 1-Comparisons between the latest deep-learning methods YOLOV3 and SSD. Angle Orthod. 2019; 89(6):903-9.

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.

Weichel F, Eisenmann U, Richter S, Hagen N, Rückschloß T, Freudlsperger C, et al. A computerassisted optimization approach for orthognathic surgery planning. Curr Dir Biomed Eng. 2019; 5(1):41-4.

Suhail Y, Upadhyay M, Chhibber A, Kshitiz. Machine learning for the diagnosis of orthodontic extractions: a computational analysis using ensemble learning. Bioengineering. 2020;7:55.

Li P, Kong D, Tang T, Su D, Yang P, Wang H, et al. Orthodontic treatment planning based on artificial neural networks. Sci Rep. 2019;9(1):2037.

Jung SK, Kim TW. New approach for the diagnosis of extractions with neural network machine learning. Am J Orthod Dentofacial Orthop. 2016;149(1):127-33.

Xie X, Wang L, Wang A. Artificial neural network modeling for deciding if extractions are necessary prior to orthodontic treatment. Angle Orthod. 2010; 80(2):262-6.

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.

Chen S, Wang L, Li G, Wu TH, Diachina S, Tejera B, et al. Machine learning in orthodontics: Introducing a 3D auto-segmentation and auto-landmark finder of CBCT images to assess maxillary constriction in unilateral impacted canine patients. Angle Orthod. 2020; 90(1):77-84.

Yu HJ, Cho SR, Kim MJ, Kim WH, Kim JW, Choi J. Automated skeletal classification with lateral cephalometry based on artificial intelligence. J Dent Res. 2020; 99(3):249–56.

Muraev AA, Tsai P, Kibardin I, Oborotistov N, Shirayeva T, Ivanov S, et al. Frontal cephalometric landmarking: humans vs artificial neural networks. Int J Comput Dent. 2020; 23(2):139–48.

Kunz F, Stellzig-Eisenhauer A, Zeman F, Boldt J. Artificial intelligence in orthodontics: evaluation of a fully automated cephalometric analysis using a customized convolutional neural network. J Orofac Orthop. 2020; 81(1):52-68.

Niño-Sandoval TC, Guevara Pérez SV, González FA, Jaque RA, Infante-Contreras C. Use of automated learning techniques for predicting mandibular morphology in skeletal class I, II and III. Forensic Sci Int. 2017; 281:187 e1- e7.

Wang L, Gao Y, Shi F, Li G, Chen KC, Tang Z, et al. Automated segmentation of dental CBCT image with prior-guided sequential random forests. Med Phys. 2016; 43(1):336-46.

Wang X, Cai B, Cao Y, Zhou C, Yang L, Liu R, et al. Objective method for evaluating orthodontic treatment from the lay perspective: an eye-tracking study. Am J Orthod Dentofacial Orthop. 2016; 150(4):601–10.

Auconi P, Scazzocchio M, Cozza P, McNamara JA Jr, Franchi L. Prediction of class III treatment outcomes through orthodontic data mining. Eur J Orthod. 2015; 37(3):257–67.

Alkhal HA, Wong RW, Rabie AB. Correlation between chronological age, cervical vertebral maturation and Fishman’s skeletal maturity indicators in southern Chinese. The Angle Orthodontist. 2008 Jul; 78(4):591-6.

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 Dec; 21(12):1222.

Kök H, Acilar AM, İzgi MS. Usage and comparison of artificial intelligence algorithms for determination of growth and development by cervical vertebrae stages in orthodontics. Progress in orthodontics. 2019 Dec; 20(1):1-0.

Cericato GO, Bittencourt MAV, Paranhos LR. Validity of the assessment method of skeletal maturation by cervical vertebrae: a systematic review and meta-analysis. Dentomaxillofac Radiol. 2015; 44(4):20140270.

Eichenberger M, Staudt CB, Pandis N, Gnoinski W, Eliades T. Facial attractiveness of patients with unilateral cleft lip and palate and of controls assessed by laypersons and professionals. European Journal of Orthodontics. 2014 Jun 1; 36(3):284-9.

Patcas R, Bernini DA, Volokitin A, Agustsson E, Rothe R, Timofte R. Applying artificial intelligence to assess the impact of orthognathic treatment on facial attractiveness and estimated age. International journal of oral and maxillofacial surgery. 2019 Jan 1; 48(1):77-83.

Lu CH, Ko EW, Liu L. Improving the video imaging prediction of postsurgical facial profiles with an artificial neural network. Journal of Dental Sciences. 2009 Sep 1; 4(3):118-29.

Thanathornwong B. Bayesian-based decision support system for assessing the needs for orthodontic treatment. Healthcare informatics research. 2018 Jan 31; 24(1):22-8.

Nieri M, Crescini A, Rotundo R, Baccetti T, Cortellini P, Pini Prato GP. Factors affecting the clinical approach to impacted maxillary canines: a Bayesian network analysis. Am J Orthod Dentofacial Orthop. 2010; 137(6):755–62.




How to Cite

Vinoth Kumar. R, Vetriselvan A, A M, P R, M.K.Karthikeyan. . Int J of Adv in Sci Res [Internet]. 2021 Oct. 3 [cited 2022 Oct. 18];7(01):e5672. Available from:



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