A Guide to AI in Orthodontics

Abstract:
The use of AI in dental practice is now becoming common. The patient’s expectation of diagnosis and treatment planning supported by AI has become mandatory in a clinic. Only if we have a basic knowledge about AI, can we comprehend what is done for the patient. This review article aims at providing an indepth knowledge what an orthodontist should know regarding AI. Though much is spoken about AI in literature, the basic background of AI still remains abtract to a dentist. This article unravels the hidden mystery behind AI. The meaning of the terms used in AI is explained with suitable examples. Starting with the paradigm shift in AI, the types of knowledge dealt is also mentioned. The basic three types of model-based studies are explained in detail. Moreover, the detailed orthodontic implications of AI are also explained with relevant references. This review article would definitely be a valuable guide for those who would like to do an AI based thesis or study.
References:
[1]. Kaplan, A, Haenlein, M.,
2019, Siri, Siri, in my hand: who’s the fairest in the land? On the
interpretations, illustrations, and implications of artificial intelligence. Bus
Horiz 62(1), 15–25.
[2]. Berat, S. A.,
Muhammet, E. T., 2021, A review of the use of artificial intelligence in
orthodontics, J Exp Clin Med., 38(S2), 157-162.
[3]. Rony, T. K.,
Aishwarya, P., Devika, G., Angeline, J., Ashwath, K., Saumya, N., 2022,
Introduction to artificial intelligence and machine learning into orthodontics:
A review, APOS Trends in Orthodontics, 12(3), July-September.
[4]. Faber, J., Faber,
C., Faber, P., 2019, Artificial intelligence in orthodontics. APOS Trends
Orthod, 9(4), 201-5.
[5]. Le Cun, Y., Bottou,
L., Bengio, Y., Haffner, P., 1998, Gradient-based learning applied to document
recognition. Proc. Of the IEEE, 86(11), 2278-2324.
[6]. Steyerberg, E. W.,
Vickera A. J., Cook, N. R., Gerds, T., Gonan, M., Obuchowski, N., Pencina, M. J.,
Kattan, M. W., 2010, Assessing the performance of prediction models: a
framework for traditional and novel measures, Epidemiology, 21(1), 128-38.
[7]. Karras, T.,
Aittala, M., Aila, T., Laine, S., 2022, elucidating the design space of
Diffusion -Based generative models, Nvidia.
[8]. Asiri, S. N.,
Tadlock, L. P, Schneiderman, E., Buschang, P. H., 2020, Applications of
artificial intelligence and machine learning in orthodontics, APOS Trends
Orthod, 10(1), 17-24
[9]. Kok, H., Izgi, M. S,
Acilar, A. M., 2021, Determination of growth and development period in
orthodontics using artificial neural network, Orthod Craniofac Res.,
24(2), 76–83
[10]. Sandoval, T. C. N.,
Perez, G., Sonia, V., Fabio, A., Gonzalez, Jaque, R., Contreras, C. I., 2017, Use of automated
learning technology for predicting mandibular morphology in skeletal class I,
II and III, Forensic Science International, 281, 187.
[11]. Rao, Y., Zhang, Q.,
Wang, X., Xue, X., Ma, W., Xu, L., Xing, S., 2024, Automated diagnosis of
adenoids hypertrophy with lateral cephalogram in children based on multiscale
local attention, Nature Portfolio,14:18619.
[12]. Shoukri, B.,
Prieto, J.C., Ruellas, A., Yatabe, M., Sugai, J., Styner, M., Zhu, H., Huang,
C., Paniagua, B., Aronovich, S., Ashman, L., Benavides, E., P. de Dumast,
Ribera, N. T., Mirabel, C., Michoud, L., Allohaibi, Z., Ioshida, M.,
Bittencourt, L., Fattori, L., Gomes, L. R., and Cevidanes, L., 2019, Minimally Invasive
Approach for Diagnosing TMJ Osteoarthritis, Journal of Dental Research, 98(5), July.
[13]. Wang, X.,
Pastewait, M., Wu, T. H, et al., 2021, 3D morphometric quantification of
maxillae and defects for patients with unilateral cleft palate via deep
learning- based CBCT image auto- segmentation, OrthodCraniofac Res, 24(2),
108–116.
[14]. Yu, X., Liu, B.,
Pei, Y., Xu, T., 2014, Evaluation of facial attractiveness for patients with
malocclusion. A machine learning technique employing Procrustes, Angle
Orthodontist, 84(3).
[15]. Kim, B. M., Kang,
B. Y., Kim, H. G., Baek, S. H., 2009, Prognosis prediction of class3
malocclusion treatment using feature wrapping method, Angle Orthodontist,
79(4).
[16]. Xie, X., Wangb, L.,
Wang, A., 2010, Artificial Neural Network Modeling for Deciding if Extractions
Are Necessary Prior to Orthodontic Treatment, Angle Orthod, 80, 262–266.
[17]. Jung, S. K, Kim, T.
W., 2016,
New
approach for diagnosis for extraction with neural network machine learning, American Journal of
Orthodontics and Dentofacial Orthopedics 149(1).
[18]. Li, P., Kong, D.,
Tang, T., Su, D., Yan, P., Wang, H., Zhao, Z., Liu, Y., 2019, orthodontic
treatment planning based on artificial neural network, Scientific Reports,
9, 20-37.
[19]. Trehan, M.,
Bhanotia, D., Shaikh, T. A., Sharma, S., Sharma, S., 2023, Artificial intelligence based automated model
for prediction of extraction using neural network machine learning: A scope and
performance analysis, Journal of Contemporary Orthodontics, 7(4), 281–286.
[20]. Okcam, M. O., Takoda, K.,
2002,
Fussy modelling for
selecting headgear types. European Journal of Orthodontics, 24, 99-106.
[21]. Choi, H., Jung, S.
K., Baek, S. H., Lim, W. H., Ahn, S. J., Yang, H., Kim, T. W., 2019, Artificial
intelligence model with neural network machine learning for the diagnosis of
orthognathic surgery, J Craniofac. Surg., 30,1986–1989.
[22]. Buschanga, P. H., Rossb, M., Shawb, S. G., Crosbyc, D., Campbelld, P. M., 2015. Predicted and actual end-of-treatment occlusion produced with aligner therapy. Angle Orthod, 85, 723–727.