AI-Driven Advancements in Orthodontics for Precision and Patient Outcomes
Abstract
:1. Introduction
2. AI in Orthodontic Treatment
2.1. Data Collection: Creating a Detailed Model
2.2. AI-Based Prediction: Understanding Tooth Movement
2.3. Personalized Treatment Planning: Tailored to the Patient
2.4. Continuous Monitoring and Adjustment: Real-Time Progress Tracking
3. Applications in Orthodontic Care
3.1. Prediction of Tooth Movement
3.2. Custom Aligner Fabrication
3.3. Optimization of Treatment Time
3.4. Enhanced Patient Monitoring and Adjustments
3.5. 3D Visualization for Treatment Planning
3.6. Accuracy and Reliability of AI-Assisted Tracing Systems in Orthodontics
3.6.1. Comparison of Manual Tracing vs. AI-Assisted Tracing
3.6.2. Need for Manual Corrections and Hybrid Approaches
4. Benefits of AI-Powered Personalized Orthodontic Treatment
4.1. Accuracy and Precision
4.2. Faster Treatment Times
4.3. Reduced Number of In-Person Visits
4.4. Cost-Effective Treatments
4.5. Enhanced Patient Experience
4.6. Technologies, Tools, and Workflow
5. The Role of the Orthodontist in AI-Driven Treatment and Its Impact on Clinical Practice
5.1. AI as a Tool, Not a Replacement: The Learning Curve and Time Considerations
5.2. The Impact of AI on Patient Trust, Engagement, and Personalized Care
6. Future Prospects of AI in Orthodontics
6.1. AI-Driven Robotics in Orthodontic Procedures
6.2. AI in Predictive Orthodontics
6.3. Real-Time 3D Printing of Orthodontic Devices
6.4. Integration with Tele-Orthodontics
7. Ethical Considerations, Data Privacy, and Limitations of AI in Orthodontics
7.1. Ethical Considerations in AI-Driven Orthodontics
7.2. Data Privacy and Security in AI-Orthodontic Applications
7.3. Limitations and Challenges of AI in Orthodontics
8. Conclusions
Author Contributions
Funding
Acknowledgments
Conflicts of Interest
References
- Xhemnica, R.; Rroço, M. Preventive and Interceptive Orthodontics Treatment. EJMN 2022, 5, 26–31. [Google Scholar] [CrossRef]
- Aljehani, W.; Alsultan, N.; Altwirki, A.; Ibrahim, A.; Almutarrid, M.; Alshahrani, L.; Alolayan, M.; Assiri, M.; Alzahrani, N.; Essa, A.; et al. Restorative approaches for managing dental anomalies. Int. J. Community Med. Public Health 2023, 10, 4977–4982. [Google Scholar] [CrossRef]
- Enzo, B. Malocclusion in orthodontics and oral health: Adopted by the General Assembly: September 2019, San Francisco, United States of America. Int. Dent. J. 2020, 70, 11–12. [Google Scholar] [CrossRef] [PubMed]
- Volovic, J.; Badirli, S.; Ahmad, S.; Leavitt, L.; Mason, T.; Bhamidipalli, S.S.; Eckert, G.; Albright, D.; Turkkahraman, H. A Novel Machine Learning Model for Predicting Orthodontic Treatment Duration. Diagnostics 2023, 13, 2740. [Google Scholar] [CrossRef]
- Albhaisi, Z.; Al-Khateeb, S.N.; Abu Alhaija, E.S. Enamel demineralization during clear aligner orthodontic treatment compared with fixed appliance therapy, evaluated with quantitative light-induced fluorescence: A randomized clinical trial. Am. J. Orthod. Dentofacial. Orthop. 2020, 157, 594–601. [Google Scholar] [CrossRef]
- Abbing, A.; Koretsi, V.; Eliades, T.; Papageorgiou, S.N. Duration of orthodontic treatment with fixed appliances in adolescents and adults: A systematic review with meta-analysis. Prog. Orthod. 2020, 21, 37. [Google Scholar] [CrossRef]
- Cattaneo, P.M.; Cornelis, M.A. Orthodontic Tooth Movement Studied by Finite Element Analysis: An Update. What Can We Learn from These Simulations? Curr. Osteoporos. Rep. 2021, 19, 175–181. [Google Scholar] [CrossRef]
- Bichu, Y.M.; Hansa, I.; Bichu, A.Y.; Premjani, P.; Flores-Mir, C.; Vaid, N.R. Applications of artificial intelligence and machine learning in orthodontics: A scoping review. Prog. Orthod. 2021, 22, 18. [Google Scholar] [CrossRef]
- Mohammad-Rahimi, H.; Nadimi, M.; Rohban, M.H.; Shamsoddin, E.; Lee, V.Y.; Motamedian, S.R. Machine learning and orthodontics, current trends and the future opportunities: A scoping review. Am. J. Orthod. Dentofacial. Orthop. 2021, 160, 170–192.e4. [Google Scholar] [CrossRef]
- Strunga, M.; Urban, R.; Surovková, J.; Thurzo, A. Artificial Intelligence Systems Assisting in the Assessment of the Course and Retention of Orthodontic Treatment. Healthcare 2023, 11, 683. [Google Scholar] [CrossRef] [PubMed]
- Dipalma, G.; Inchingolo, A.D.; Inchingolo, A.M.; Piras, F.; Carpentiere, V.; Garofoli, G.; Azzollini, D.; Campanelli, M.; Paduanelli, G.; Palermo, A.; et al. Artificial Intelligence and Its Clinical Applications in Orthodontics: A Systematic Review. Diagnostics 2023, 13, 3677. [Google Scholar] [CrossRef] [PubMed]
- Gurgel, M.; Alvarez, M.A.; Aristizabal, J.F.; Baquero, B.; Gillot, M.; Al Turkestani, N.; Miranda, F.; Castillo, A.A.; Bianchi, J.; de Oliveira Ruellas, A.C.; et al. Automated artificial intelligence-based three-dimensional comparison of orthodontic treatment outcomes with and without piezocision surgery. Orthod. Craniofac. Res. 2024, 27, 321–331. [Google Scholar] [CrossRef] [PubMed]
- Kondody, R.T.; Patil, A.; Devika, G.; Jose, A.; Kumar, A.; Nair, S. Introduction to artificial intelligence and machine learning into orthodontics: A review. APOS Trends Orthod. 2022, 12, 214–220. [Google Scholar] [CrossRef]
- Hung, K.; Yeung, A.W.K.; Tanaka, R.; Bornstein, M.M. Current Applications, Opportunities, and Limitations of AI for 3D Imaging in Dental Research and Practice. Int. J. Environ. Res. Public Health 2020, 17, 4424. [Google Scholar] [CrossRef]
- Jihed, M.; Dallel, I.; Tobji, S.; Amor, A.B. The Impact of Artificial Intelligence on Contemporary Orthodontic Treatment Planning—A Systematic Review and Meta-Analysis. Sch. J. Dent. Sci. 2022, 9, 70–87. [Google Scholar] [CrossRef]
- Li, P.; Kong, D.; Tang, T.; Su, D.; Yang, P.; Wang, H.; Zhao, Z.; Liu, Y. Orthodontic Treatment Planning based on Artificial Neural Networks. Sci. Rep. 2019, 9, 2037. [Google Scholar] [CrossRef]
- Grippaudo, C. 3D Diagnosis in Dentistry. Open. Dent. J. 2022, 16, E187421062203010. [Google Scholar] [CrossRef]
- Jabri, M.A.; Wu, S.; Pan, Y.; Wang, L. An overview on the veracity of intraoral digital scanning system and utilization of iTero scanner for analyzing orthodontic study models both In-Vivo and Ex-Vivo. Niger. J. Clin. Pract. 2021, 24, 1–7. [Google Scholar] [CrossRef]
- Francisco, I.; Ribeiro, M.P.; Marques, F.; Travassos, R.; Nunes, C.; Pereira, F.; Caramelo, F.; Paula, A.B.; Vale, F. Application of Three-Dimensional Digital Technology in Orthodontics: The State of the Art. Biomimetics 2022, 7, 23. [Google Scholar] [CrossRef]
- Trehan, M.; Bhanotia, D.; Shaikh, T.A.; Sharma, S.; Sharma, S. Artificial intelligence-based automated model for prediction of extraction using neural network machine learning: A scope and performance analysis. J. Contemp. Orthod. 2023, 7, 281–286. [Google Scholar] [CrossRef]
- Shimizu, Y.; Tanikawa, C.; Kajiwara, T.; Nagahara, H.; Yamashiro, T. The validation of orthodontic artificial intelligence systems that perform orthodontic diagnoses and treatment planning. Eur. J. Orthod. 2022, 44, 436–444. [Google Scholar] [CrossRef] [PubMed]
- Albalawi, F.; Alamoud, K.A. Trends and Application of Artificial Intelligence Technology in Orthodontic Diagnosis and Treatment Planning—A Review. Appl. Sci. 2022, 12, 11864. [Google Scholar] [CrossRef]
- Perillo, L.; d’Apuzzo, F.; De Gregorio, F.; Grassia, V.; Barbetti, M.; Cugliari, G.; Nucci, L.; Castroflorio, T. Factors Affecting Patient Compliance during Orthodontic Treatment with Aligners: Motivational Protocol and Psychological Well-Being. Turk. J. Orthod. 2023, 36, 87–93. [Google Scholar] [CrossRef]
- Yu, X.; Li, G.; Zheng, Y.; Gao, J.; Fu, Y.; Wang, Q.; Huang, L.; Pan, X.; Ding, J. ‘Invisible’ orthodontics by polymeric ‘clear’ aligners molded on 3D-printed personalized dental models. Regen. Biomater. 2022, 9, rbac007. [Google Scholar] [CrossRef]
- Nguyen, T.; Jackson, T. 3D technologies for precision in orthodontics. Semin. Orthod. 2018, 24, 386–392. [Google Scholar] [CrossRef]
- Hannequin, R.; Ouadi, E.; Racy, E.; Moreau, N. Clinical follow-up of corticotomy-accelerated Invisalign orthodontic treatment with Dental Monitoring. Am. J. Orthod. Dentofacial. Orthop. 2020, 158, 878–888. [Google Scholar] [CrossRef]
- Thurzo, A.; Kurilová, V.; Varga, I. Artificial Intelligence in Orthodontic Smart Application for Treatment Coaching and Its Impact on Clinical Performance of Patients Monitored with AI-TeleHealth System. Healthcare 2021, 9, 1695. [Google Scholar] [CrossRef] [PubMed]
- Wafaie, K.; Rizk, M.Z.; Basyouni, M.E.; Daniel, B.; Mohammed, H. Tele-orthodontics and sensor-based technologies: A systematic review of interventions that monitor and improve compliance of orthodontic patients. Eur. J. Orthod. 2023, 45, 450–461. [Google Scholar] [CrossRef]
- 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. Orthod. Craniofac. Res. 2023, 26, 102–110. [Google Scholar] [CrossRef]
- Khanagar, S.B.; Al-Ehaideb, A.; Vishwanathaiah, S.; Maganur, P.C.; Patil, S.; Naik, S.; Baeshen, H.A.; Sarode, S.S. Scope and performance of artificial intelligence technology in orthodontic diagnosis, treatment planning, and clinical decision-making—A systematic review. J. Dent. Sci. 2021, 16, 482–492. [Google Scholar] [CrossRef]
- Caruso, S.; Caruso, S.; Pellegrino, M.; Skafi, R.; Nota, A.; Tecco, S. A Knowledge-Based Algorithm for Automatic Monitoring of Orthodontic Treatment: The Dental Monitoring System. Two Cases. Sensors 2021, 21, 1856. [Google Scholar] [CrossRef] [PubMed]
- Kunz, F.; Stellzig-Eisenhauer, A.; Boldt, J. Applications of Artificial Intelligence in Orthodontics—An Overview and Perspective Based on the Current State of the Art. Appl. Sci. 2023, 13, 3850. [Google Scholar] [CrossRef]
- Monill-González, A.; Rovira-Calatayud, L.; d’Oliveira, N.G.; Ustrell-Torrent, J.M. Artificial intelligence in orthodontics: Where are we now? A scoping review. Orthod. Craniofac. Res. 2021, 24, 6–15. [Google Scholar] [CrossRef]
- Faber, J.; Faber, C.; Faber, P. Artificial intelligence in orthodontics. APOS Trends Orthod. 2019, 9, 201–205. [Google Scholar] [CrossRef]
- Gaonkar, P.; Mohammed, I.; Ribin, M.; Kumar, D.C.; Thomas, P.A.; Saini, R. Assessing the Impact of AI-Enhanced Diagnostic Tools on the Treatment Planning of Orthodontic Cases: An RCT. J. Pharm. Bioallied Sci. 2024, 16, S1798–S1800. Available online: https://pubmed.ncbi.nlm.nih.gov/38882868/ (accessed on 24 January 2024). [CrossRef]
- Alam, M.K.; Alanazi, D.S.A.; Alruwaili, S.R.; Alderaan, R.A.I. Assessment of AI Models in Predicting Treatment Outcomes in Orthodontics. J. Pharm. Bioallied Sci. 2024, 16, S540–S542. [Google Scholar] [CrossRef]
- Ramasubbu, N.; Kasim, S.A.V.; Thavarajah, R.; Rengarajan, K. Applying Artificial Intelligence to Predict the Outcome of Orthodontic Treatment. APOS Trends Orthod. 2024, 14, 264–272. [Google Scholar] [CrossRef]
- Park, J.-A.; Moon, J.-H.; Lee, J.-M.; Cho, S.J.; Seo, B.-M.; Donatelli, R.E.; Lee, S.-J. Does Artificial Intelligence Predict Orthognathic Surgical Outcomes Better Than Conventional Linear Regression Methods? Angle Orthod. 2024, 94, 549–556. [Google Scholar] [CrossRef]
- Almarhoumi, A.A. Accuracy of Artificial Intelligence in Predicting Facial Changes Post-Orthognathic Surgery: A Comprehensive Scoping Review. J. Clin. Exp. Dent. 2024, 16, e624–e633. [Google Scholar] [CrossRef]
- Nordblom, N.F.; Büttner, M.; Schwendicke, F. Artificial Intelligence in Orthodontics: Critical Review. J. Dent. Res. 2024, 103, 577–584. [Google Scholar] [CrossRef]
- Usmanova, Z.; Sunbuloglu, E. An in-silico approach to modeling orthodontic tooth movement using stimulus-induced external bone adaptation. J. Mech. Behav. Biomed. Mater. 2021, 124, 104827. [Google Scholar] [CrossRef] [PubMed]
- Hussain, M.A.; Fatima, S.; Reddy, K.K.; Ramya, Y.; Betha, S.P.; Kauser, A.; Shetty, C. Artificial intelligence in orthodontics: A review. Int. J. Health Sci. 2022, 6, 9378–9383. [Google Scholar] [CrossRef]
- Cho, S.; Moon, J.; Ko, D.; Lee, J.; Park, J.; Donatelli, R.; Lee, S. Orthodontic treatment outcome predictive performance differences between artificial intelligence and conventional methods. Angle Orthod. 2024, 94, 557–565. [Google Scholar] [CrossRef] [PubMed]
- Yadav, P.K.; Verma, S.K.; Bais, D.R.S. Impact of Technology on Orthodontic Practice. J. Dent. Spec. 2024, 12, 25. [Google Scholar] [CrossRef]
- Kazimierczak, N.; Kazimierczak, W.; Serafin, Z.; Nowicki, P.; Nożewski, J.; Janiszewska-Olszowska, J. AI in Orthodontics: Revolutionizing Diagnostics and Treatment Planning—A Comprehensive Review. J. Clin. Med. 2024, 13, 344. [Google Scholar] [CrossRef]
- Regalado-Bazán, C.-F.; Espichan-Salazar, A.-C.; Arriola-Guillén, L. Comparison of relapse of orthodontic treatment following aligner versus conventional fixed appliance treatment: A systematic review. J. Clin. Exp. Dent. 2024, 16, e586–e594. [Google Scholar] [CrossRef]
- Retrouvey, J.M.; Gonzalez, J.; Shumilov, E.; Abdallah, M.N. Orthodontics 4.0: Artificial intelligence and its applications in orthodontic diagnosis and treatment planning. Kieferorthopädie 2023, 37, 285–297. [Google Scholar]
- Dot, G.; Licha, R.; Goussard, F.; Sansalone, V. A new protocol to accurately track long-term orthodontic tooth movement and support patient-specific numerical modeling. J. Biomech. 2021, 129, 110760. [Google Scholar] [CrossRef]
- Haouili, N.; Kravitz, N.D.; Vaid, N.R.; Ferguson, D.J.; Makki, L. Has Invisalign improved? A prospective follow-up study on the efficacy of tooth movement with Invisalign. Am. J. Orthod. Dentofacial. Orthop. 2020, 158, 420–425. [Google Scholar] [CrossRef]
- El-Angbawi, A.; McIntyre, G.; Fleming, P.S.; Bearn, D. Non-surgical adjunctive interventions for accelerating tooth movement in patients undergoing orthodontic treatment. Cochrane Database Syst. Rev. 2023, 6, CD010887. [Google Scholar] [CrossRef]
- Tartaglia, G.M.; Mapelli, A.; Maspero, C.; Santaniello, T.; Serafin, M.; Farronato, M.; Caprioglio, A. Direct 3D Printing of Clear Orthodontic Aligners: Current State and Future Possibilities. Materials 2021, 14, 1799. [Google Scholar] [CrossRef] [PubMed]
- Elshazly, T.; Keilig, L.; Alkabani, Y.; Ghoneima, A.; Abuzayda, M.; Talaat, W.; Talaat, S.; Bourauel, C.P. Potential Application of 4D Technology in Fabrication of Orthodontic Aligners. Front. Mater. 2022, 8, 794536. [Google Scholar] [CrossRef]
- Sabbagh, H.; Heger, S.M.; Stocker, T.; Baumert, U.; Wichelhaus, A.; Hoffmann, L. Accuracy of 3D Tooth Movements in the Fabrication of Manual Setup Models for Aligner Therapy. Materials 2022, 15, 3853. [Google Scholar] [CrossRef]
- Likitmongkolsakul, U.; Smithmaitrie, P.; Samruajbenjakun, B.; Aksornmuang, J. Development and Validation of 3D Finite Element Models for Prediction of Orthodontic Tooth Movement. Int. J. Dent. 2018, 2018, 4927503. [Google Scholar] [CrossRef]
- Lombardo, L.; Palone, M.; Carlucci, A.; Siciliani, G. Clear aligner hybrid approach: A case report. J. World Fed. Orthod. 2020, 9, 32–43. [Google Scholar] [CrossRef]
- Ke, Y.; Zhu, Y.; Zhu, M. A comparison of treatment effectiveness between clear aligner and fixed appliance therapies. BMC Oral Health 2019, 19, 24. [Google Scholar] [CrossRef]
- Cassetta, M.; Guarnieri, R.; Altieri, F. The combined use of clear aligners and computer-guided piezocision: A case report with a 2-year follow-up. Int. J. Comput. Dent. 2020, 23, 57–71. [Google Scholar]
- Nishimoto, S.; Kawai, K.; Fujiwara, T.; Ishise, H.; Kakibuchi, M. Locating cephalometric landmarks with multi-phase deep learning. J. Dent. Health Oral Res. 2023, 4, 1–13. [Google Scholar] [CrossRef]
- Shetty, V.G.; Rai, R.; Shetty, K.N. Artificial intelligence and machine learning: The new paradigm in orthodontic practice. Int. J. Orthod. Rehabil. 2020, 11, 175–179. [Google Scholar] [CrossRef]
- Moylan, H.B.; Carrico, C.K.; Lindauer, S.J.; Tüfekçi, E. Accuracy of a smartphone-based orthodontic treatment-monitoring application: A pilot study. Angle Orthod. 2019, 89, 727–733. [Google Scholar] [CrossRef]
- Sosiawan, A.; Jordana, J.; Dhywinanda, D.E.; Salim, J.F.; Ramadhani, N.F.; Nurdiansyah, R.; Ardani, I.G.A.W.; Nugraha, A.P. Artificial intelligence driven dental monitoring and surveillance of malocclusion treatment in orthodontic patients. WJARR 2022, 16, 049–053. [Google Scholar] [CrossRef]
- Soboku, T.; Motegi, E.; Sueishi, K. Effect of Different Bracket Prescriptions on Orthodontic Treatment Outcomes Measured by Three-dimensional Scanning. Bull. Tokyo Dent. Coll. 2019, 60, 69–80. [Google Scholar] [CrossRef] [PubMed]
- Shen, F.; Liu, J.; Li, H.; Fang, B.; Ma, C.; Hao, J.; Feng, Y.; Zheng, Y. OrthoGAN. Image Generation for Teeth Orthodontic Visualization. arXiv 2022, arXiv:2212.14162. [Google Scholar] [CrossRef]
- Chiang, Y.C.; Wu, F.; Ko, S.H. Effective Patient-Dentist Communication with a Simulation System for Orthodontics. Healthcare 2023, 11, 1433. [Google Scholar] [CrossRef]
- Chang, R.; Jie, W.; Thakur, N.; Zhao, Z.; Pahwa, R.; Yang, X. A Unified and Adaptive Continual Learning Method for Feature Segmentation of Buried Packages in 3D XRM Images. In Proceedings of the 2024 IEEE 74th Electronic Components and Technology Conference (ECTC), Denver, CO, USA, 28–31 May 2024; pp. 1872–1879. [Google Scholar] [CrossRef]
- Kumar, A.; Pandian, S. Evaluation of Accuracy and Reliability of Artificial Intelligence-Based Fully Automated And Semi-Automated Cephalometric Analysis Software In Comparison With Manual Cephalometric Analysis. South East. Eur. J. Public Health 2024, XXV, 137–144. [Google Scholar] [CrossRef]
- Tsolakis, I.; Tsolakis, A.; Elshebiny, T.; Matthaios, S.; Palomo, J. Comparing a Fully Automated Cephalometric Tracing Method to a Manual Tracing Method for Orthodontic Diagnosis. J. Clin. Med. 2022, 11, 6854. [Google Scholar] [CrossRef]
- Leevan, P.; Tania, S.; Rathore, S.; Missier, D.; Shaga, B. Comparison of Accuracy and reliability of Automated tracing Android app with Conventional and Semiautomated Computer aided tracing software for cephalometric Analysis—A cross-sectional study. Int. J. Orthod. Rehabil. 2023, 13, 39–51. [Google Scholar] [CrossRef]
- Subramanian, A.; Chen, Y.; Almalki, A.; Sivamurthy, G.; Kafle, D. Cephalometric Analysis in Orthodontics Using Artificial Intelligence—A Comprehensive Review. Biomed. Res. Int. 2022, 2022, 1880113. [Google Scholar] [CrossRef]
- Zhang, M.; Ning, N.; Hong, Y.; Zhou, M.; Gong, X.; Zeng, L.; Wu, Y.; Ye, H.; Kang, T.; Chen, X. Digital working process in diagnosis, treatment planning and fabrication of personalized orthodontic appliances. Digit. Med. 2023, 9, e00004. [Google Scholar] [CrossRef]
- Rafiq, A.; Konda, P. 3D printing: Changing the landscape of orthodontics. IP Indian J. Orthod. Dentofac. Res. 2024, 10, 149–157. [Google Scholar] [CrossRef]
- Al-Dboush, R.; Esfahani, A.N.; El-Bialy, T. Impact of photobiomodulation and low-intensity pulsed ultrasound adjunctive interventions on orthodontic treatment duration during clear aligner therapy. Angle Orthod. 2021, 91, 619–625. [Google Scholar] [CrossRef] [PubMed]
- Olteanu, C.; Pop, A.; Boicioc, B.; Chibelean, M.; Muntean, A.; Vlad, G.I.; Păcurar, M. Factors influencing the duration of orthodontic treatment. Ro. J. Stomatol. 2020, 66, 110–115. [Google Scholar] [CrossRef]
- Jaber, S.T.; Hajeer, M.Y.; Burhan, A.S. The Effectiveness of In-house Clear Aligners and Traditional Fixed Appliances in Achieving Good Occlusion in Complex Orthodontic Cases: A Randomized Control Clinical Trial. Cureus 2022, 14, e30147. [Google Scholar] [CrossRef] [PubMed]
- Gao, M.; Yan, X.; Zhao, R.; Shan, Y.; Chen, Y.; Jian, F.; Long, H.; Lai, W. Comparison of pain perception, anxiety, and impacts on oral health-related quality of life between patients receiving clear aligners and fixed appliances during the initial stage of orthodontic treatment. Eur. J. Orthod. 2021, 43, 353–359. [Google Scholar] [CrossRef]
- Chapuis, M.; Lafourcade, M.; Puech, W.; Guillerm, G.; Faraj, N. Animating and Adjusting 3D Orthodontic Treatment Objectives. In Proceedings of the GRAPP 2022-17th International Conference on Computer Graphics Theory and Applications, Virtual, 6–8 February 2022; pp. 60–67. [Google Scholar] [CrossRef]
- Abutayyem, H.; Alsalam, A.; Iqbal, R.; Alkhabuli, J.; Mohamed, S. Robotic Use in Orthodontics: Literature Review. Oral Health Dent. Sci. 2019, 3, 1–5. [Google Scholar] [CrossRef]
- Tomášik, J.; Zsoldos, M.; Majdáková, K.; Fleischmann, A.; Oravcová, Ľ.; Sónak Ballová, D.; Thurzo, A. The Potential of AI-Powered Face Enhancement Technologies in Face-Driven Orthodontic Treatment Planning. Appl. Sci. 2024, 14, 7837. [Google Scholar] [CrossRef]
- Al-Hassiny, A. Fundamentals of Computer-Aided Design (CAD) in Dental Healthcare: From Basics to Beyond. In 3D Printing in Oral Health Science: Applications and Future Directions; Springer International Publishing: Cham, Switzerland, 2022; pp. 93–119. [Google Scholar] [CrossRef]
- Lee, J.M.; Moon, J.H.; Park, J.A.; Kim, J.H.; Lee, S.J. Factors influencing the development of artificial intelligence in orthodontics. Orthod. Craniofac. Res. 2024, 27, 6–12. [Google Scholar] [CrossRef]
- 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. [Google Scholar] [CrossRef]
- Sangalli, L.; Savoldi, F.; Dalessandri, D.; Bonetti, S.; Gu, M.; Signoroni, A.; Paganelli, C. Effects of remote digital monitoring on oral hygiene of orthodontic patients: A prospective study. BMC Oral Health 2021, 21, 435. [Google Scholar] [CrossRef]
- Hansa, I.; Katyal, V.; Semaan, S.J.; Coyne, R.; Vaid, N.R. Artificial Intelligence Driven Remote Monitoring of orthodontic patients: Clinical applicability and rationale. Semin. Orthod. 2021, 27, 138–156. [Google Scholar] [CrossRef]
- Homsi, K.; Ramachandran, V.; Del Campo, D.M.; Del Campo, L.M.; Kusnoto, B.; Atsawasuwan, P.; Viana, G.; Oubaidin, M.; Allareddy, V.; Elnagar, M.H. The use of teleorthodontics during the COVID-19 pandemic and beyond-perspectives of patients and providers. BMC Oral Health 2023, 23, 490. [Google Scholar] [CrossRef] [PubMed]
- Abu Arqub, S.; Al-Moghrabi, D.; Kuo, C.L.; Da Cunha Godoy, L.; Uribe, F. Perceptions and utilization of tele-orthodontics: A survey of the members of the American Association of Orthodontists. Prog. Orthod. 2024, 25, 16. [Google Scholar] [CrossRef] [PubMed]
- Lo Giudice, A.; Ronsivalle, V.; Venezia, P.; Ragusa, R.; Palazzo, G.; Leonardi, R.; Lazzara, A. Teleorthodontics: Where Are We Going? From Skepticism to the Clinical Applications of a New Medical Communication and Management System. Int. J. Dent. 2022, 2022, 7301576. [Google Scholar] [CrossRef]
- Shang, Z.; Chauhan, V.; Devi, K.; Patil, S. Artificial Intelligence, the Digital Surgeon: Unravelling Its Emerging Footprint in Healthcare—The Narrative Review. J. Multidiscip. Healthc. 2024, 17, 4011–4022. [Google Scholar] [CrossRef]
- Maart, R.D.; Mulder, R. Ethical Considerations for Artificial Intelligence in Dentistry. S. Afr. Dent. J. 2024, 79, 260–262. [Google Scholar] [CrossRef]
- Islam, M. Ethical Considerations in AI: Navigating the Complexities of Bias and Accountability. J. Artif. Intell. Gen. Sci. 2024, 3, 2–30. [Google Scholar] [CrossRef]
- Olaoye, G. Ethical Considerations in Artificial Intelligence Development. Filos. Ref. Zhurnal 2024. [CrossRef]
- Gala, K.M. Ethical and Legal Considerations in AI-Driven Health Cybersecurity. Int. J. Sci. Res. Comput. Sci. Eng. Inf. Technol. 2024, 10, 682–690. [Google Scholar] [CrossRef]
- Dhopte, A.; Bagde, H. Smart Smile: Revolutionizing Dentistry With Artificial Intelligence. Cureus 2023, 15, e41227. [Google Scholar] [CrossRef]
- Verma, S.; Paliwal, N.; Yadav, K.; Vashist, P.C. Ethical Considerations of Bias and Fairness in AI Models. In Proceedings of the 2024 2nd International Conference on Disruptive Technologies (ICDT), Greater Noida, India, 15–16 March 2024; pp. 818–823. [Google Scholar] [CrossRef]
- Anyanwu, E.C.; Okongwu, C.C.; Olorunsogo, T.O.; Ayo-Farai, O.; Osasona, F.; Daraojimba, O.D. Artificial Intelligence in Healthcare: A Review of Ethical Dilemmas and Practical Applications. Int. Med. Sci. Res. J. 2024, 4, 126–140. [Google Scholar] [CrossRef]
Patient Category | Common Orthodontic Needs | How AI Enhances Treatment | Key Benefits | Challenges and Considerations |
---|---|---|---|---|
Children (6–12 years) [36,40] | Early intervention for developing malocclusions, space maintainers, monitoring growth patterns. | AI assists in early diagnosis through predictive modeling, detects jaw discrepancies, and automates cephalometric analysis. | Early treatment reduces need for extensive future interventions, improves facial growth, enhances treatment efficiency. | Requires long-term monitoring; AI predictions for growing patients must account for skeletal changes. |
Teenagers (13–18 years) [10,40] | Comprehensive orthodontic treatment (braces, aligners), correction of malocclusions, compliance monitoring. | AI optimizes bracket/aligner placement, predicts treatment progress, and improves compliance tracking with remote monitoring. | Reduces treatment time, increases adherence through AI-driven reminders, improves aesthetics and confidence. | Teen compliance varies; AI-based remote tracking depends on patient engagement. |
Adults (19–40 years) [37,41] | Aesthetic-focused orthodontics, mild-to-moderate malocclusions, clear aligners, relapse correction. | AI customizes aligners for precise tooth movement, shortens treatment time, integrates with digital smile design. | Enables minimally invasive treatment, enhances efficiency, provides virtual treatment simulations for better patient decision-making. | AI must account for periodontal health and bone density variations in adults. |
Older Adults (40+ years) [42,43] | Orthodontic treatment with periodontal considerations, pre-prosthetic orthodontics, jaw alignment issues. | AI assesses bone loss, suggests adaptive treatment plans, and monitors periodontal risk factors in treatment planning. | Enhances orthodontic feasibility in complex cases, prevents worsening of periodontal issues, facilitates interdisciplinary care. | AI’s accuracy is influenced by dental history, bone density variability, and coexisting oral conditions. |
Patients with Severe Skeletal Malocclusions [36,44] | Skeletal discrepancies requiring orthognathic surgery, severe overbites/underbites, facial asymmetry. | AI models simulate treatment outcomes, predict surgical-orthodontic needs, and assist in virtual surgical planning. | Improves surgical precision, allows better pre-treatment planning, provides visual predictions for patient understanding. | AI cannot replace surgeon expertise; accuracy depends on detailed 3D imaging integration. |
Patients with Mild to Moderate Malocclusions [40,45] | Crowding, spacing, mild bite issues suitable for clear aligners or short-term braces. | AI-driven aligner design optimizes force application, predicts faster treatment timelines, and automates case selection. | Reduces treatment duration, enhances patient comfort, minimizes need for refinements. | AI performance varies depending on malocclusion complexity and patient compliance. |
Patients with Orthodontic Relapse [10,46] | Post-treatment shifting of teeth, need for retainers, minor corrective aligner treatments. | AI detects minor shifts in tooth position, recommends retainer adjustments, and optimizes minor corrective movements. | Prevents further relapse, improves long-term stability, reduces need for extensive re-treatment. | Requires accurate post-treatment monitoring and patient adherence to retainer use. |
AI Application in Orthodontics | Machine Learning Model Used | Function and Role in Orthodontics | Advantages | Limitations |
---|---|---|---|---|
Cephalometric Landmark Detection and Tracing [10,40] | Convolutional Neural Networks (CNNs), Deep Learning Algorithms | Automates the identification of anatomical landmarks on cephalometric X-rays, aiding in diagnosis and treatment planning. | Reduces manual tracing time, enhances accuracy, minimizes intra-operator variability. | AI may misidentify landmarks in cases of poor image quality or anatomical variation, requiring orthodontist intervention. |
Tooth and Jaw Segmentation in 3D Imaging [69] | U-Net, Region-Based CNN (R-CNN) | AI-driven segmentation of teeth and jawbones in CBCT, intraoral scans, and panoramic radiographs. | Provides detailed 3D models for accurate diagnosis and treatment planning, enhances aligner or bracket positioning. | AI accuracy depends on the quality of training data; complex cases (e.g., supernumerary teeth) may pose challenges. |
AI-Powered Tooth Movement Prediction [35] | Recurrent Neural Networks (RNNs), Long Short-Term Memory (LSTM) Networks | Predicts how teeth will move under various orthodontic forces based on patient-specific biomechanical data. | Enables personalized treatment planning, reduces the need for mid-treatment refinements, optimizes force application. | Predictions may not always account for biological variability in tooth movement, requiring real-world validation. |
Automated Orthodontic Treatment Planning [35,40] | Support Vector Machines (SVM), Gradient Boosting Algorithms (e.g., XGBoost) | AI-assisted optimization of treatment plans for braces and clear aligners based on patient-specific factors. | Increases efficiency in case analysis, allows orthodontists to evaluate multiple treatment simulations quickly. | AI-generated plans require orthodontist oversight; AI may not fully account for patient preferences or lifestyle factors. |
Custom Aligner Fabrication [26,52] | Generative Adversarial Networks (GANs), Reinforcement Learning | AI optimizes the design of clear aligners based on 3D scans, ensuring precise force application for tooth movement. | Improves accuracy in aligner staging, reduces production costs, shortens overall treatment time. | Requires integration with 3D printing technology; miscalculations in force application can lead to mid-treatment refinements. |
AI-Driven Bracket Positioning for Fixed Appliances [60] | CNN-Based Object Detection (YOLO, Faster R-CNN) | AI suggests optimal bracket placement on teeth, improving efficiency in fixed orthodontic treatments. | Enhances consistency and precision, reduces errors in manual placement. | May require manual adjustments in complex cases; AI models need continual refinement. |
AI-Based Caries and Periodontal Disease Detection [10,35] | Deep Learning CNNs, Transfer Learning Models | AI detects early signs of caries, periodontal disease, and bone loss from intraoral scans and X-rays. | Enables early intervention, reducing long-term complications and improving oral health. | AI may misinterpret artifacts in imaging; false positives or negatives require human validation. |
AI-Assisted Orthodontic Remote Monitoring [10,35] | Vision Transformers (ViTs), Deep CNNs | AI analyzes patient-submitted images and videos to track treatment progress remotely. | Reduces in-person visits, increases treatment adherence through real-time monitoring. | Image quality and patient compliance in capturing clear intraoral images may impact AI accuracy. |
AI in 3D Printing for Orthodontics [26,52] | Generative Design Algorithms, Evolutionary AI | AI-driven customization of orthodontic appliances, including aligners, retainers, and splints, using 3D printing technology. | Reduces appliance production time, enables personalized treatment. | Integration with AI-driven design is still evolving, requiring orthodontist supervision. |
AI-Driven Robotics in Orthodontics [71] | Reinforcement Learning, Motion Planning Algorithms | AI-assisted robotic systems for wire bending, bracket placement, and appliance customization. | Increases precision, reduces manual effort, improves consistency in orthodontic procedures. | High implementation costs; robotics in orthodontics is still in developmental stages. |
Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content. |
© 2025 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
Share and Cite
Olawade, D.B.; Leena, N.; Egbon, E.; Rai, J.; Mohammed, A.P.E.K.; Oladapo, B.I.; Boussios, S. AI-Driven Advancements in Orthodontics for Precision and Patient Outcomes. Dent. J. 2025, 13, 198. https://doi.org/10.3390/dj13050198
Olawade DB, Leena N, Egbon E, Rai J, Mohammed APEK, Oladapo BI, Boussios S. AI-Driven Advancements in Orthodontics for Precision and Patient Outcomes. Dentistry Journal. 2025; 13(5):198. https://doi.org/10.3390/dj13050198
Chicago/Turabian StyleOlawade, David B., Navami Leena, Eghosasere Egbon, Jeniya Rai, Aysha P. E. K. Mohammed, Bankole I. Oladapo, and Stergios Boussios. 2025. "AI-Driven Advancements in Orthodontics for Precision and Patient Outcomes" Dentistry Journal 13, no. 5: 198. https://doi.org/10.3390/dj13050198
APA StyleOlawade, D. B., Leena, N., Egbon, E., Rai, J., Mohammed, A. P. E. K., Oladapo, B. I., & Boussios, S. (2025). AI-Driven Advancements in Orthodontics for Precision and Patient Outcomes. Dentistry Journal, 13(5), 198. https://doi.org/10.3390/dj13050198