DOI : https://doi.org/10.18517/ijaseit.15.2.20157

Transforming Orthodontics: Exploring Evolution and Applications of Artificial Intelligence

HeeJeong Jasmine Lee (1), Nurul Syahira Mohamad Zamani (2), Asma Ashari (3), Reuben Axel Wee Ming How (4)
(1) College of Information and Communication Engineering, Sungkyunkwan University, Suwon, Republic of Korea
(2) Department of Electrical, Electronic and Systems Engineering, Faculty of Engineering and Built Environment, Universiti Kebangsaan Malaysia, Bangi, Selangor, Malaysia
(3) Department of Family Oral Health, Faculty of Dentistry, Universiti Kebangsaan Malaysia, Kuala Lumpur, Malaysia
(4) Visivest Corporation, T2-6&7, 2nd Floor, Wisma SPS, 32 Jalan Imbi, Kuala Lumpur, Malaysia
Fulltext View | Download
How to cite (IJASEIT) :
[1]
H. J. Lee, N. S. Mohamad Zamani, A. Ashari, and R. A. W. M. How, “Transforming Orthodontics: Exploring Evolution and Applications of Artificial Intelligence”, Int. J. Adv. Sci. Eng. Inf. Technol., vol. 15, no. 2, pp. 395–401, Apr. 2025.
Citation Format :
The rapid advancement of Artificial Intelligence (AI) technology, fueled by the increasing availability of extensive datasets, enhanced computing power, and the development of sophisticated algorithms, has led to its widespread integration into various aspects of daily life. AI is making significant inroads in dentistry, particularly in specialties such as orthodontics, prosthodontics, oral and maxillofacial surgery, and periodontics, where it has begun to transform traditional practices. This comprehensive review synthesizes the current literature to explore the application of AI in orthodontics. The focus is primarily on utilizing AI for image-based diagnostic tasks, including the analysis of radiographic and optical images, while addressing the potential applications, benefits, and inherent limitations of AI for non-image-based tasks within the field. AI demonstrates considerable efficacy in image-based diagnostics within orthodontics, significantly enhancing the accuracy and efficiency of the analyses. However, the application of AI to non-image-based tasks is hindered by several challenges, including the scarcity of quantitative data, the inherent complexity of oral health conditions, and the substantial computational power required to process intricate 3D data. Over the past few decades, orthodontics has undergone profound transformations driven by AI-influenced advancements, such as the development of aesthetically improved treatment alternatives, the seamless integration of digital workflows, and the emergence of cutting-edge imaging techniques. These advancements underscore the potential of AI to revolutionize orthodontic care further, offering innovative solutions that enhance both patient outcomes and clinical practices, while paving the way for future developments in the field.

N. S. M. Zamani et al., “Distributed force measurement and mapping using pressure-sensitive film and image processing for active and passive aligners on orthodontic attachments,” IEEE Access, vol. 10, pp. 52853–52865, 2022, doi: 10.1109/access.2022.3175210.

B. S. Akdeniz and M. E. Tosun, “A review of the use of artificial intelligence in orthodontics,” Journal of Experimental and Clinical Medicine (Turkey), vol. 38, no. 3s, pp. 157–162, 2021, doi:10.52142/omujecm.38.si.dent.13.

H. Il Choi et al., “Artificial intelligent model with neural network machine learning for the diagnosis of orthognathic surgery,” Journal of Craniofacial Surgery, vol. 30, no. 7, pp. 1986–1989, 2019, doi:10.1097/scs.0000000000005650.

H. Kim, E. Shim, J. Park, Y. J. Kim, U. Lee, and Y. Kim, “Web-based fully automated cephalometric analysis by deep learning,” Comput Methods Programs Biomed, vol. 194, p. 105513, 2020, doi:10.1016/j.cmpb.2020.105513.

C. Zhao et al., “Automatic recognition of cephalometric landmarks via multi-scale sampling strategy,” Heliyon, vol. 9, no. 6, Jun. 2023, doi:10.1016/j.heliyon.2023.e17459.

W. M. Talaat et al., “An artificial intelligence model for the radiographic diagnosis of osteoarthritis of the temporomandibular joint,” Sci Rep, vol. 13, no. 1, Dec. 2023, doi: 10.1038/s41598-023-43277-6.

H. Bao et al., “Evaluating the accuracy of automated cephalometric analysis based on artificial intelligence,” BMC Oral Health, vol. 23, no. 1, Dec. 2023, doi: 10.1186/s12903-023-02881-8.

H. Ding, J. Wu, W. Zhao, J. P. Matinlinna, M. F. Burrow, and J. K. H. Tsoi, “Artificial intelligence in dentistry—A review,” Frontiers in Dental Medicine, vol. 4, pp. 1–13, 2023, doi:10.3389/fdmed.2023.1085251.

X. Xie, L. Wang, and A. Wang, “Artificial neural network modeling for deciding if extractions are necessary prior to orthodontic treatment,” Angle Orthodontist, vol. 80, no. 2, pp. 262–266, 2010, doi:10.2319/111608-588.1.

M. Li, “Comprehensive review of backpropagation neural networks,” Academic Journal of Science and Technology , vol. 9, no. 1, pp. 1–5, 2024, doi: 10.54097/51y16r47.

C. Tanikawa and T. Yamashiro, “Development of novel artificial intelligence systems to predict facial morphology after orthognathic surgery and orthodontic treatment in Japanese patients,” Sci Rep, vol. 11, no. 1, p. 15853, 2021, doi: 10.1038/s41598-021-95002-w.

N. S. M. Zamani, E. Yoon Choong Hoe, A. B. Huddin, W. M. D. Wan Zaki, and Z. Abd Hamid, “Deep learning for an automated image-based stem cell classification,” Jurnal Kejuruteraan, vol. 35, no. 5, pp. 1181–1189, Sep. 2023, doi: 10.17576/jkukm-2023-35(5)-18.

B. Thanathornwong, “Bayesian-based decision support system for assessing the needs for orthodontic treatment,” Healthc Inform Res, vol. 24, no. 1, pp. 22–28, 2018, doi: 10.4258/hir.2018.24.1.22.

Z. Cui et al., “TSegNet: An efficient and accurate tooth segmentation network on 3D dental model,” Med Image Anal, vol. 69, p. 101949, 2021, doi: 10.1016/j.media.2020.101949.

Z. Cui et al., “A fully automatic AI system for tooth and alveolar bone segmentation from cone-beam CT images,” Nat Commun, vol. 13, no. 1, pp. 1–11, 2022, doi: 10.1038/s41467-022-29637-2.

G. Wei et al., “Dense representative tooth landmark/axis detection network on 3D model,” Comput Aided Geom Des, vol. 94, p. 102077, 2022, doi: 10.1016/j.cagd.2022.102077.

M. Mitchell, “Abstraction and analogy-making in artificial intelligence,” Ann N Y Acad Sci, vol. 1505, no. 1, pp. 79–101, 2021, doi: 10.1111/nyas.14619.

S. Kumar, I. Dasgupta, R. Marjieh, N. D. Daw, J. D. Cohen, and T. L. Griffiths, “Disentangling abstraction from statistical pattern matching in human and machine learning,” PLoS Comput Biol, vol. 19, no. 8, pp. 1–21, 2023, doi: 10.1371/journal.pcbi.1011316.

S. F. Ahmed et al., “Deep learning modelling techniques: current progress, applications, advantages, and challenges,” Artif Intell Rev, vol. 56, no. 11, pp. 13521–13617, Nov. 2023, doi: 10.1007/s10462-023-10466-8.

K. Takada, M. Yagi, and H. Eriko, “Computational formulation of orthodontic tooth-extraction decisions: Part I: To extract or not to extract,” Angle Orthodontist, vol. 79, no. 5, pp. 885–891, 2009, doi:10.2319/081908-436.1.

M. Yagi, H. Ohno, and K. Takada, “Computational formulation of orthodontic tooth-extraction decisions: Part II: Which tooth should be extracted?,” Angle Orthodontist, vol. 79, no. 5, pp. 892–898, 2009, doi:10.2319/081908-439.1.

S. Wang et al., “Automatic laser profile recognition and fast tracking for structured light measurement using deep learning and template matching,” Measurement (Lond), vol. 169, Feb. 2021, doi:10.1016/j.measurement.2020.108362.

D. Luo, W. Zeng, J. Chen, and W. Tang, “Deep learning for automatic image segmentation in stomatology and its clinical application,” Front Med Technol, vol. 3, 2021, doi: 10.3389/fmedt.2021.767836.

M. M. Taye, “Theoretical understanding of convolutional neural network: Concepts, architectures, applications, future directions,” Computation, vol. 11, no. 3, pp. 1–23, 2023, doi:10.3390/computation11030052.

A. A. Tulbure, A. A. Tulbure, and E. H. Dulf, “A review on modern defect detection models using DCNNs – Deep convolutional neural networks,” J Adv Res, vol. 35, pp. 33–48, 2022, doi:10.1016/j.jare.2021.03.015.

B. M. Kim, B. Y. Kang, H. G. Kim, and S. H. Baek, “Prognosis prediction for class III malocclusion treatment by feature wrapping method,” Angle Orthodontist, vol. 79, no. 4, pp. 683–691, 2009, doi:10.2319/071508-371.1.

L. Gong, S. Xie, Y. Zhang, M. Wang, and X. Wang, “Hybrid feature selection method based on feature subset and factor analysis,” IEEE Access, vol. 10, pp. 120792–120803, 2022, doi:10.1109/access.2022.3222812.

B. Shan, M. Werger, W. Huang, and D. B. Giddon, “Quantitating the art and science of esthetic clinical success,” Jun. 01, 2021, Elsevier Inc. doi: 10.1016/j.ejwf.2021.03.004.

G. Dipalma et al., “Artificial intelligence and its clinical applications in orthodontics: A systematic review,” Diagnostics, vol. 13, no. 24, Dec. 2023, doi: 10.3390/diagnostics13243677.

R. T. Kondody, A. Patil, G. Devika, A. Jose, A. Kumar, and S. Nair, “Introduction to artificial intelligence and machine learning into orthodontics: A review,” APOS Trends in Orthodontics, vol. 12, no. 3, pp. 214–220, Jul. 2022, doi: 10.25259/APOS_60_2021.

T. A. Siddiqui, R. H. Sukhia, and D. Ghandhi, “Artificial intelligence in dentistry, orthodontics and orthognathic surgery: A literature review,” J Pak Med Assoc, vol. 72, no. 1, pp. 91–96, 2022, doi:10.47391/jpma.aku-18.

Y. M. Bichu, I. Hansa, A. Y. Bichu, P. Premjani, C. Flores-Mir, and N. R. Vaid, “Applications of artificial intelligence and machine learning in orthodontics: a scoping review,” Prog Orthod, vol. 22, no. 18, pp. 1–11, 2021, doi: 10.1186/s40510-021-00361-9.

V. Allareddy et al., “Call for algorithmic fairness to mitigate amplification of racial biases in artificial intelligence models used in orthodontics and craniofacial health,” Dec. 01, 2023, John Wiley and Sons Inc. doi: 10.1111/ocr.12721.

C. M. Mörch et al., “Artificial intelligence and ethics in dentistry: A scoping review,” J Dent Res, vol. 100, no. 13, pp. 1452–1460, 2021, doi: 10.1177/00220345211013808.

M. I. Khan, S. M. Laxmikanth, T. Gopal, and P. K. Neela, “Artificial intelligence and 3D printing technology in orthodontics: future and scope,” AIMS Biophys, vol. 9, no. 3, pp. 182–197, 2022, doi:10.3934/biophy.2022016.

Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.

Authors who publish with this journal agree to the following terms:

    1. Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under a Creative Commons Attribution License that allows others to share the work with an acknowledgement of the work's authorship and initial publication in this journal.
    2. Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgement of its initial publication in this journal.
    3. Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work (See The Effect of Open Access).