Release Date: 2024-06-17

Applications of Artifical Intelligence in Orthodontics

Demet Suer (Author)

Release Date: 2024-06-17

Artificial intelligence (AI) has emerged as a transformative technology with significant implications across various fields, including orthodontics. This paper explores the multifaceted applications of AI in orthodontics, highlighting its potential to enhance diagnostic accuracy, treatment planning, and patient outcomes. AI technologies, including machine learning (ML) and deep learning, facilitate automated analysis of complex data, aiding [...]

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    Work TypeBook Chapter
    Published inComplementary Medicine with New Approaches
    First Page215
    Last Page230
    DOIhttps://doi.org/10.69860/nobel.9786053359418.16
    Page Count16
    Copyright HolderNobel Tıp Kitabevleri
    Licensehttps://nobelpub.com/publish-with-us/copyright-and-licensing

    Artificial intelligence (AI) has emerged as a transformative technology with significant implications across various fields, including orthodontics. This paper explores the multifaceted applications of AI in orthodontics, highlighting its potential to enhance diagnostic accuracy, treatment planning, and patient outcomes. AI technologies, including machine learning (ML) and deep learning, facilitate automated analysis of complex data, aiding in precise diagnosis and efficient treatment strategies. Key applications discussed include AI-assisted patient monitoring, cephalometric analysis, extraction decision-making, prediction of un-erupted teeth sizes, diagnosis of impacted teeth, and bone age determination. The integration of AI in orthodontics promises to improve clinical decision-making, reduce human error, and personalize treatment plans, ultimately leading to better patient care. Despite being in the early stages, AI demonstrates substantial promise in revolutionizing orthodontic practices, paving the way for more accurate, efficient, and predictable treatment outcomes.

    Demet Suer (Author)
    Assistant Professor, Dicle University
    https://orcid.org/0000-0002-1496-9694
    3Dr. Demet SÜER TÜMEN graduated from Dicle University Faculty of Dentistry in 2004 and completed her PhD in 2011 at Dicle University Health Sciences Institute, Department of Orthodontics, with her thesis titled "Comparison of Cephalometric Changes in Upper Incisor Intrusion with Different Methods," earning the title of Doctor of Science. In the same year, she was awarded the title of Orthodontic Specialist by the Ministry of Health according to the relevant legal provisions. Between 2011-2022, she worked as an Orthodontic Specialist at Eskişehir Oral and Dental Health Hospital and Diyarbakır Oral and Dental Health Hospital under the Ministry of Health. In January 2023, she was appointed as an Assistant Professor to the vacant position in the Dental Prosthesis Technologies Program of the Department of Dental Services at Dicle University Atatürk Health Services Vocational School and was assigned to the Department of Orthodontics at Dicle University Faculty of Dentistry. Dr. Demet SÜER TÜMEN has authored book chapters, research articles published in international and national journals, reviews, and case reports related to the field of Orthodontics, as well as oral and poster presentations, and completed BAP and TÜBİTAK projects. Her areas of interest include clear aligner treatments, mini-screw applications, orthodontic interventions with functional appliances and orthopedic devices during the growth and development period, and digital dentistry and 3D printing technologies.

    • Ergen M. What is Artificial Intelligence? Technical Considerations and Future Perception. Anatolian Journal of Cardiology. 2019;22:5-7. doi:10.14744/AnatolJCardiol.2019.79091

    • Faber J, Faber C, Faber P. Artificial intelligence in orthodontics. APOS Trends in Orthod. 2019;9:201-205.

    • Khanagar SB, Al-Ehaideb A, Maganur PC, et al. Developments, application, and performance of artificial intelligence in dentistry: A systematic review. J Dent Sci. 2021;16:508- 522. doi:10.1016/j.jds.2020.06.019

    • Rajaraman V, McCarthy J. Father of artificial intelligence. Reson. 2014:198-207.

    • National Research Council. Funding a Revolution: Government Support for Computing Research. Washington, DC: The National Academies Press; 1999:302.

    • Bellman R. Artificial Intelligence: Can Computers Think? Thomson Course Technology; 1978:146.

    • Bor S, Kotan S. Ortodontide yapay zeka etkileri. J Dent Fac Usak Univ. 2023;2(1):14-19.

    • Liu J, Chen Y, Li S, Zhao Z, Wu Z. Machine learning in orthodontics: Challenges and perspectives. Adv Clin Exp Med. 2021;30(10). doi:10.17219/acem/138702

    • Akst J. A primer: Artificial intelligence versus neural networks. Inspiring Innovation: The Scientist Exploring Life. 2019:65802.

    • Rabun˜al JR, Dorado J. Artificial Neural Networks in Real-Life Applications. IGI Global; 2005:166-346.

    • Büyük SK, Hatal S. Artificial intelligence and machine learning in orthodontics. Ortadogu Tip Derg. 2019;11(4):517-523. doi:10.21601/ortadogutipdergisi.547782

    • Machoy ME, Szyszka-Sommerfeld L, Vegh A, Gedrange T, Woźniak K. The ways of using machine learning in dentistry. Adv Clin Exp Med. 2020;29(3):375-384. doi:10.17219/ acem/115083

    • Genç E. Ortodontide yapay zeka ve dijital tedaviler. In: Uzel I, Ayhan H, eds. Güncel Ort- odonti ve Pedodonti Çalışmaları II. 1st ed. Ankara: Akademisyen Kitapevi; 2023. ISBN: 978-625-8399-44-8. doi:10.37609/akya.1275

    • Holder LB, Haque MM, Skinner MK. Machine learning for epigenetics and future medical applications. Epigenetics. 2017;12(7):505-514. doi:10.1080/15592294.2017.1329068

    • Schwendicke F, Samek W, Krois J. Artificial intelligence in dentistry: Chances and challenges. J Dent Res. 2020;99(7):769-774. doi:10.1177/0022034520915714

    • Tekeli A. Yapay zekanın ortodontik tedavideki rolü. Kırıkkale Uni Tip Derg. 2023;25(2):340-346. doi:10.24938/kutfd.1335382

    • Murata S, Lee C, Tanikawa C, Date S. Towards a fully automated diagnostic system for orthodontic treatment in dentistry. In: Proceedings of the IEEE 13th International Con- ference on e-Science. 2017:1-8.

    • Leonardi R, Giordano D, Maiorana F, Spampinato C. Automatic cephalometric analysis. Angle Orthod. 2008;78:145-151.

    • Moylan HB, Carrico CK, Lindauer SJ, Tüfekçi E. Accuracy of a smartphone-based orthodontic treatment-monitoring application: A pilot study. Angle Orthod. 2019;89:727-733.

    • 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;81:52-68.

    • Hwang HW, Moon JH, Kim MG, Donatelli RE, Lee SJ. Evaluation of automated cephalo- metric analysis based on the latest deep learning method. Angle Orthod. 2021;91:329-335.

    • Del Real A, Del Real O, Sardina S, Oyonarte R. Use of automated artificial intelligence to predict the need for orthodontic extractions. Korean J Orthod. 2022;52:102-111.

    • Noureen A, Anusre A, Premila D, Karthikeyan MK, Rajkumar P. Artificial intelligence in orthodontic diagnosis and treatment planning: An overview. Int J Creative Res Thoughts. 2022;10(10):e233-e244.

    • Niño-Sandoval TC, Pérez SVG, González FA, Jaque RA, Contreras CI. Use of automated learning techniques for predicting mandibular morphology in skeletal class I, II, and III. Forensic Sci Int. 2017;281:187.e1-187.e7. doi:10.1016/j.forsciint.2017.10.004

    • Niño-Sandoval TC, Pérez SVG, González FA, Jaque RA, Contreras CI. An automatic method for skeletal patterns classification using craniomaxillary variables on a Colombi- an population. Forensic Sci Int. 2016;261:159.e1-159.e6. doi:10.1016/j.forsciint.2015.12.025

    • Takada K, Yagi M, Horiguchi E. Computational formulation of orthodontic tooth-ex- traction decisions. Part I: To extract or not to extract. Angle Orthod. 2009;79(5):885-891.

    • 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. doi:10.1093/ejo/cjr042

    • Nieri M, Crescini A, Rotundo R, Bacetti 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):755-762. doi:10.1016/j.ajodo.2008.08.028

    • Kim DW, Kim TW, Lee SH, et al. Prediction of hand-wrist maturation stages based on cervical vertebrae images using artificial intelligence. Orthod Craniofac Res. 2021;24:6875.

    • Kim H, Lee SJ, Kim KH, et al. Prediction of Fishman’s skeletal maturity indicators using artificial intelligence. Sci Rep. 2023;13:5870.

    • Mathew R, Kurian N, Reji B, et al. Neural networks for classification of cervical vertebrae maturation: A systematic review. Angle Orthod. 2022;92:796-804.

    • Fishman LS. Radiographic evaluation of skeletal maturation: A clinically oriented method based on hand-wrist films. Angle Orthod. 1982;52(2):88-112.

    • Robbins JW. Color characterization of porcelain veneers. Quintessence Int. 1991;22:853856.

    • El-Askary FS, El-Banna AH, van Noort R. Immediate vs delayed repair bond strength of a nanohybrid resin composite. J Adhes Dent. 2012;14:265-274.

    • Durka-Zajac M, Mitus-Kenig M, Derwich M, Marcinkowska-Mitus A, Loboda M. Radiological indicators of bone age assessment in cephalometric images: Review. Pol J Radiol. 2016;81:347-353. doi:10.12659/PJR.895921

    • McNamara JA Jr, Franchi L. The cervical vertebral maturation method: A user’s guide. Angle Orthod. 2018;88(2):133-143. doi:10.2319/111517-787.1

    • Yu HJ, Cho SR, Kim MJ, et al. Automated skeletal classification with lateral cephalometry based on artificial intelligence. J Dent Res. 2020;99(3):249-256. doi:10.1177/0022034520901715

    • Sokic E, Tiro A, Sokic-Begovic E, Nakas E, Tiro A. Semi-automatic assessment of cervi- cal vertebral maturation stages using cephalograph images and centroid based clustering. Acta Stomatol Croat. 2012;46(4):280-290.

    • Albalawi F, Alamoud KA. Trends and application of artificial intelligence technology in orthodontic diagnosis and treatment planning: A review. Appl Sci. 2022;12:11864.

    • Bianchi J, Mendonca G, Gillot M, et al. Three-dimensional digital applications for implant space planning in orthodontics: A narrative review. J World Fed Orthod. 2022;11(6):207- 215.

    • Kravitz ND, Kusnoto B, BeGole E, Obrez A, Agran B. How well does Invisalign work? A prospective clinical study evaluating the efficacy of tooth movement with Invisalign. Am J Orthod Dentofacial Orthop. 2009;135(1):27-35.

    • Auconi P, Gili T, Capuani S, et al. The validity of machine learning procedures in orthodontics: What is still missing? J Pers Med. 2022;12(6):957.

    • De La Garza-Ramos MA, Ipiña-Lozano HH, Cano-Verdugo G, Nakagoshi-Cepeda MAA, Liu Y. Application of robotics in orthodontics: A systematic review. Cureus. 2024;16(4):e58555. doi:10.7759/cureus.58555

    • van Riet TCT, Chin Jen Sem KTH, Ho JTF, et al. Robot technology in dentistry, part two of a systematic review: An overview of initiatives. Dent Mater. 2021;37(8):1227-1236.

    • Adel S, Zaher A, El Harouni N, Venugopal A, Premjani P, Vaid N. Robotic applications in orthodontics: Changing the face of contemporary clinical care. Biomed Res Int. 2021;2021:9954615. doi:10.1155/2021/9954615

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