Authors : Dr. Yash Kayastha; Dr. Ajay Kantilal Kubavat; Dr. Khyati Viral Patel; Dr. Helly Patel; Dr. Pinal Patel

Volume/Issue : Volume 10 - 2025, Issue 9 - September

Google Scholar : https://tinyurl.com/4wvhwj34

Scribd : https://tinyurl.com/3duhdpkw

DOI : https://doi.org/10.38124/ijisrt/25sep389

Note : A published paper may take 4-5 working days from the publication date to appear in PlumX Metrics, Semantic Scholar, and ResearchGate.

Note : Google Scholar may take 30 to 40 days to display the article.

Abstract : Artificial Intelligence (AI) is rapidly transforming orthodontics, evolving from a supplementary aid to a potential cornerstone of modern dental practice. By leveraging the capabilities of machine learning (ML), deep learning (DL), and artificial neural networks (ANNs), orthodontists can now process and interpret massive multimodal datasets with unprecedented precision and efficiency.1,2,4,15These datasets range from traditional cephalometric radiographs to complex three-dimensional imaging modalities such as cone-beam computed tomography (CBCT) and intraoral scans.3 Recent advancements have demonstrated that AI-powered diagnostic tools can equal, and in some cases surpass, the accuracy of seasoned clinicians in specific tasks such as cephalometric landmark identification or growth prediction.5 Beyond diagnostics, AI offers powerful applications in treatment planning, biomechanical tooth movement simulations, surgical planning, patient compliance monitoring, and even the customization of orthodontic appliances through integration with 3D printing technologies.12,13,14 This expanded review aims to provide an in-depth exploration of AI’s principles, current clinical applications, limitations, and future directions in orthodontics. The discussion draws upon literature from 2019 to 2025, case examples, and real-world clinical adoption scenarios. Ethical, legal, and technical considerations—such as patient data privacy, algorithmic bias, regulatory compliance, and seamless integration into clinical workflows—are analyzed in detail.16,17,18,19 Ultimately, we envision a future in which AI-enhanced orthodontics merges data-driven decision-making with the artistry of clinical expertise, resulting in care that is individualized, efficient, and firmly evidence-based.

Keywords : Artificial Intelligence, Orthodontics, Machine Learning, Neural Networks, Cephalometrics, CBCT, Tele-Orthodontics, 3D Printing.

References :

1. Hölbling E, Kraxner L, et al. Deep learning for cephalometric landmark detection: systematic review and meta-analysis. Orthod Craniofac Res. 2021;24(2):32-42. doi:10.1111/ocr.12446.
2. Hwang H-W, Moon J-H, et al. Evaluation of automated cephalometric analysis based on the latest deep learning method. Angle Orthod. 2021;91(3):329-335. doi:10.2319/021220-100.1.
3. Bozkurt TO, Tuncer E, et al. Multiclass U-Net-based segmentation of craniofacial structures on CBCT. Biomed Eng Online. 2021;20(1):1-21. doi:10.1186/s12938-021-00910-9.
4. Sathyanarayana S, Zhou G, et al. DL-based automatic cephalometric landmarking in 3D: a systematic review and meta-analysis. Radiol Med. 2023;128:1100-1114. doi:10.1007/s11547-023-01629-2.
5. Baccetti E, et al. Systematic review of AI for cervical vertebral maturation staging and growth prediction. Diagnostics (Basel). 2025;15(5):1063.
6. Rokhshad R, Lopez MA, et al. Diagnostic accuracy of AI in temporomandibular disorders: systematic review & meta-analysis. J Oral Rehabil. 2024;51(10):1045-1064. doi:10.1111/joor.13637.
7. Moon J-H, Kim M-G, et al. Automated photograph-cephalogram image integration using AI models. Angle Orthod. 2024;94(6):595-603.
8. Xie X, Wang F, et al. Artificial neural network modeling for deciding if extractions are necessary prior to orthodontic treatment. Angle Orthod. 2010;80(2):262-266.
9. Jung S-K, Kim T-W. New approach for the diagnosis of extractions with neural network machine learning. Am J Orthod Dentofacial Orthop. 2016;149(5):716-728.
10. Mason T, Kelly KM, et al. A machine learning model for orthodontic extraction/non-extraction decision. Int Orthod. 2023;21(3):100759. doi:10.1016/j.ortho.2023.100759.
11. Kim M, Kim J, et al. Use of automated artificial intelligence to predict the need for extractions for orthodontic treatment. Korean J Orthod. 2022;52(2):102-111. doi:10.4041/kjod.2022.52.2.102.
12. Hansa I, Semaan SJ, et al. Effectiveness of AI-driven Dental Monitoring in clear aligner treatment. Orthod Craniofac Res. 2023;26(3):123-134.
13. Keim RG, et al. Effectiveness of dental monitoring system in orthodontics: a systematic review. J Orthod. 2023;50(3):251-264.
14. Mangano F, et al. Remote digital monitoring during the retention phase reduces emergency appointments: a feasibility study. Prog Orthod. 2022;23(1):13.
15. Kunz F, et al. AI in orthodontics: evaluation of a fully automated cephalometric analysis using a customized CNN. J Orofac Orthop. 2020;81(1):52-68.
16. Mörch CM, Atsu SS, et al. Artificial Intelligence and Ethics in Dentistry: A Scoping Review. J Dent Res. 2021;100(12):1333-1342.
17. Brady AP, Neri E. Artificial Intelligence in Radiology—Ethical Considerations. Diagnostics (Basel). 2020;10(4):231.
18. ITU FG-AI4H. Ethical considerations of artificial intelligence in dentistry: a checklist framework. Geneva: ITU; 2025.
19. Frontiers in Big Data (ESR/ACR). Ethics of AI in Radiology: societal implications and fairness. Front Big Data. 2022;5:850383.