Authors : Dr. Pachaiyappan G.; Dr. Madhusudhan V.; Dr. Shailaja A. M.; Dr. Chethan Kumar D.; Dr. Padmashri Narayanan

Volume/Issue : Volume 10 - 2025, Issue 10 - October

Google Scholar : https://tinyurl.com/5y8hubky

Scribd : https://tinyurl.com/y472bz9a

DOI : https://doi.org/10.38124/ijisrt/25oct240

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) has emerged as a transformative force across healthcare, and orthodontics is no exception. By mimicking human intelligence and learning from large volumes of clinical data, AI technologies are increasingly being applied to diagnostic processes, treatment planning, and patient monitoring. In orthodontics, AI has shown particular promise in areas such as cephalometric landmark identification, malocclusion classification, growth prediction, and the customization of appliances like aligners and brackets. These tools not only enhance diagnostic accuracy but also streamline workflows, improve patient engagement, and allow for more personalized treatment strategies. Remote monitoring systems further extend orthodontic care beyond the clinic, increasing accessibility and convenience for patients. However, the integration of AI into orthodontics also presents challenges, including issues of data privacy, algorithmic transparency, and ethical responsibility. Despite these hurdles, ongoing innovations suggest that AI will continue to evolve as an essential partner in orthodontic practice, augmenting clinical expertise rather than replacing it. This review aims to provide a comprehensive overview of the applications, benefits, limitations, and future prospects of AI in orthodontics, highlighting its growing role in shaping the future of diagnosis, treatment, and patient-centered care.

Keywords : Artificial Intelligence; Orthodontics; Cephalometric Analysis; Machine Learning; Teleorthodontics.

References :

1. Schwendicke F, Chaurasia A, Arsiwala L, Lee J H, Elhennawy K, Jost-Brinkmann P G, Demarco F, Krois J, “Deep Learning for Cephalometric Landmark Detection: Systematic Review and Meta-Analysis,” Clinical Oral Investigations, vol. 25, no. 7, pp. 4299-4309, 2021. https://doi.org/10.1007/s00784-021-04057-2
2. [2]. Serafin M, Baldini B, Cabitza F, Carrafiello G, Baselli G, Del Fabbro M, Sforza C, Caprioglio A, Tartaglia G M, “Accuracy of Automated 3D Cephalometric Landmarks by Deep Learning Algorithms: Systematic Review and Meta-Analysis,” La Radiologia Medica, vol. 128, no. 5, pp. 544-555, 2023. https://doi.org/10.1007/s11547-023-01629-2
3. Baig, N., Gyasudeen, K.S., Bhattacharjee, T. et al. Comparative evaluation of commercially available AI-based cephalometric tracing programs. BMC Oral Health 24, 1241 (2024). https://doi.org/10.1186/s12903-024-05032-9
4. Subramanian AK, Chen Y, Almalki A, Sivamurthy G, Kafle D. Cephalometric Analysis in Orthodontics Using Artificial Intelligence-A Comprehensive Review. Biomed Res Int. 2022 Jun 16;2022:1880113. doi: 10.1155/2022/1880113. PMID: 35757486; PMCID: PMC9225851.
5. Wood T, Anigbo J O, Eckert G, Stewart K T, Dundar M M, Turkkahraman H, “Prediction of post-pubertal mandibular length and Y axis of growth using machine learning techniques,” Diagnostics, vol. 13, no. 9, p. 1553, 2023. DOI:10.3390/diagnostics13091553
6. Shoari S A, Sadrolashrafi S V, Sohrabi A, Afrouzian R, Ebrahimi P, Kouhsoltani M, Soltani M K, “Estimating Mandibular Growth Stage Based on Cervical Vertebral Maturation in Lateral Cephalometric Radiographs Using Artificial Intelligence,” Progress in Orthodontics, vol. 25, no. 1, p. 28, 2024.
7. Popova T, Stocker T, Khazaei Y, Mangold C, Langenbach G, Peltomäki T, “Influence of growth structures and fixed appliances on automated cephalometric landmark recognition with CNN,” BMC Oral Health, vol. 23, p. 274, 2023. DOI:10.1186/s12903-023-02984-2
8. Nordblom N F, Büttner M, Schwendicke F, “Artificial Intelligence in Orthodontics: Critical Review,” Journal of Dental Research, vol. 103, no. 6, pp. 577-584, 2024.
9. Jung S K, Kim T W, “New Approach for the Diagnosis of Extractions with Neural Network Machine Learning,” American Journal of Orthodontics and Dentofacial Orthopedics, vol. 149, no. 1, pp. 127–133, 2016. https://doi.org/10.1016/j.ajodo.2015.07.030
10. Real A D, Real O D, Sardina S, Oyonarte R, “Use of Automated Artificial Intelligence to Predict the Need for Orthodontic Extractions,” Korean Journal of Orthodontics, vol. 52, no. 2, pp. 102-111, 2022.
11. Volovic J, Lazarov L, Videnovic M, Tosic G, Manojlovic A, Antic S, “A Novel Machine Learning Model for Predicting Orthodontic Treatment Duration,” Diagnostics, vol. 13, no. 17, p. 2740, 2023. https://doi.org/10.3390/diagnostics13172740
12. Kim Y H, Lee C, Ha E G, Choi Y J, Han S S, “A Fully Deep Learning Model for the Automatic Identification of Cephalometric Landmarks,” Imaging Science in Dentistry, vol. 51, no. 3, pp. 299-306, 2021.
13. Song Y, Qiao X, Iwamoto Y, Chen Y-w, “Automatic Cephalometric Landmark Detection on X-ray Images Using a Deep-Learning Method,” Applied Sciences, vol. 10,no.7,p.2547,2020. https://doi.org/10.3390/app10072547
14. Lee H, Cho J M, Ryu S, Park S H, Kim J H, “Automatic Identification of Posteroanterior Cephalometric Landmarks Using a Novel Deep Learning Algorithm: A Comparative Study with Human Experts,” Scientific Reports, vol. 13, p. 15506, 2023. https://doi.org/10.1038/s41598-023-42870-z
15. Lee J H, Yu H J, Kim M J, Kang J M, Cho H J, “Automated Cephalometric Landmark Detection with Confidence Regions Using Bayesian Convolutional Neural Networks,” BMC Oral Health, vol. 20, p. 270, 2020. https://doi.org/10.1186/s12903-020-01256-7
16. Huang C, Wang J, Wang S, Zhang Y, “A Review of Deep Learning in Dentistry,” Neurocomputing, vol. 554, p.126629,2023. https://doi.org/10.1016/j.neucom.2023.126629
17. Ruiz D C, Mureșanu S, Du X, Elgarba B M, Fontenele R C, Jacobs R, “Unveiling the Role of Artificial Intelligence Applied to Clear Aligner Therapy: A Scoping Review,” Journal of Dentistry, vol. 154, p. 105564,2025. https://doi.org/10.1016/j.jdent.2025.105564
18. Feng H, Song W, Li R, Yang L, Chen X, Guo J, Liao X, Ni L, Zhu Z, Chen J, Pei X, Li Y, Wang J, “A Fully Integrated Orthodontic Aligner With Force Sensing Ability for Machine Learning-Assisted Diagnosis,” Advanced Science, vol. 12, no. 2, p. e2411187, 2025. https://doi.org/10.1002/advs.202411187
19. Lee Y T, Wu T H, Lin M L, Ko C C, “Machine (Deep) Learning and Finite Element Modeling for Orthodontic Applications,” in Machine Learning in Dentistry, Springer, pp. 183–188, 2021.
20. Li H, Chen Y, Wang Q, Gong X, Lei Y, Tian J, Gao X, “Convolutional Neural Network-Based Automatic Cervical Vertebral Maturation Classification Method,” Dentomaxillofacial Radiology, vol. 51, no. 6, p. 20220070, 2022. https://doi.org/10.1259/dmfr.20220070
21. [21]. Myers M, Brown M D, Badirli S, Eckert G J, Johnson D H, Turkkahraman H, “Long-Term Predictive Modelling of the Craniofacial Complex Using Machine Learning on 2D Cephalometric Radiographs,” International Dental Journal, vol. 75, no. 1, pp. 236-247, 2025.
22. Larkin A, Brown D, Wang S, Zhao J, “Accuracy of Artificial Intelligence-Assisted Growth Prediction in Orthodontics,” Orthodontic Craniofacial Research, 2024. https://doi.org/10.1111/ocr.12764
23. Strunga M, Urban R, Surovková J, Thurzo A, “Artificial Intelligence Systems Assisting in the Assessment of the Course and Retention of Orthodontic Treatment,” Healthcare, vol. 11, no. 5, p. 683, 2023. https://doi.org/10.3390/healthcare11050683.
24. Snider V, Homsi K, Kusnoto B, Atsawasuwan P, Viana G, Allareddy V, Gajendrareddy P, Elnagar M H, “Effectiveness of AI-Driven Remote Monitoring Technology in Improving Oral Hygiene During Orthodontic Treatment,” Orthodontics & Craniofacial Research, vol. 26, suppl. 1, pp. 102–110, 2023. https://doi.org/10.1111/ocr.12666.
25. Liu T Y, Lee K H, Mukundan A, Karmakar R, Dhiman H, Wang H C, “AI in Dentistry: Innovations, Ethical Considerations, and Integration Barriers,” Bioengineering (Basel), vol. 12, no. 9, p. 928, 2025.
26. Guo X, Shao Y, “AI-Driven Dynamic Orthodontic Treatment Management: Personalized Progress Tracking and Adjustments—A Narrative Review,” Frontiers in Dental Medicine, vol. 6, p. 1612441, 2025. https://doi.org/10.3389/fdmed.2025.1612441
27. Lee M, Allareddy V, Rampa S, Elnagar M, Oubaidin M, Yadav S, Rengasamy Venugopalan S, “Applications and Challenges of Implementing Artificial Intelligence in Orthodontics: A Primer for Orthodontists,” Seminars in Orthodontics, vol. 30, no. 3, pp. 72-76, 2024.
28. Li Z, Wang L, “Multi-Task Reinforcement Learning and Explainable AI-Driven Platform for Personalized Planning and Clinical Decision Support in Orthodontic-Orthognathic Treatment,” Scientific Reports, vol. 15, p. 24502, 2025.