Shaping smiles with AI: a comprehensive review of artificial intelligence in orthodontics
Keywords:
Artificial intelligence, orthodontics, machine learning, deep learning, cephalometric analysis, predictive modeling, treatment outcomes, personalized orthodontics, clinical decision-making, AI-driven tools
Abstract
The application of artificial intelligence (AI) to orthodontics is transforming the discipline by making it possible to plan treatments, make diagnoses, and monitor patients with greater accuracy. The use of AI technologies in orthodontic practice, including computer vision, deep learning, and machine learning, is examined in this paper. Personalized therapy protocols, predictive modeling for treatment results, and automated cephalometric analysis are important areas of emphasis. Current issues including data privacy, algorithmic bias, and the requirement for standardized data sets are explored alongside AI's promise to increase productivity, improve patient outcomes, and support clinical decision-making. The paper also provides a research roadmap and discusses current developments in AI-driven orthodontic tools. Orthodontics can gain from increased workflow automation and more accurate treatments by using AI into clinical practice.
References
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31. Murdoch B. Privacy and artificial intelligence: challenges for protecting health information in a new era. BMC Med Ethics. 2021 Sep 15;22(1):122. doi: 10.1186/s12910-021-00687-3.
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48. Gandedkar NH, Wong MT, Darendeliler MA: Role of virtual reality (vr), augmented reality (ar) and artificial intelligence (ai) in tertiary education and research of orthodontics: an insight. Elsevier;2021;69–77.
49. Moussa R, Alghazaly A, Althagafi N, Eshky R, Borzangy S. Effectiveness of virtual reality and interactive simulators on dental education outcomes: systematic review. Eur J Dent. 2022 Feb;16(1):14-31. doi: 10.1055/s-0041-1731837.
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51. Strunga M, Urban R, Surovková J, Thurzo A. Artificial intelligence systems assisting in the assessment of the course and retention of orthodontic treatment. Healthcare (Basel). 2023 Feb 25;11(5):683. doi: 10.3390/healthcare11050683.
52. Javaid M, Haleem A, Pratap Singh R, Suman R, Rab S. Significance of machine learning in healthcare: features, pillars and applications. Int J Intell Netw. 2022;3:58–73.
2. 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 Jan 7;13(2):344. doi: 10.3390/jcm13020344.
3. Alzaid N, Ghulam O, Albani M, Alharbi R, Othman M, Taher H, et al. Revolutionizing dental care: A comprehensive review of artificial intelligence applications among various dental specialties. Cureus. 2023 Oct 14;15(10):e47033. doi: 10.7759/cureus.47033.
4. Anil S, Porwal P, Porwal A. Transforming dental caries diagnosis through artificial intelligence-based techniques. Cureus. 2023 Jul 11;15(7):e41694.
doi: 10.7759/cureus.41694.
5. Aichert, A.; Wein, W.; Ladikos, A.; Reichl, T.; Navab, N. Image-based tracking of the teeth for orthodontic augmented reality. Comput. Vision 2012, 15, 601–608.
6. Nordblom NF, Büttner M, Schwendicke F. Artificial intelligence in orthodontics: critical review. J dent res. 2024 jun;103(6):577-584. doi: 10.1177/00220345241235606.
7. Bichu YM, Hansa I, Bichu AY, Premjani P, Flores-Mir C, Vaid NR. Applications of artificial intelligence and machine learning in orthodontics: a scoping review. Prog Orthod. 2021 Jul 5;22(1):18. doi: 10.1186/s40510-021-00361-9.
8. S. Bhat, G. K. Birajdar and M. D. Patil, "A comprehensive survey of deep learning algorithms and applications in dental radiograph analysis", Healthcare Analytics, vol. 100282, 2023.
9. Wang X, Alqahtani KA, Van den Bogaert T, Shujaat S, Jacobs R, Shaheen E. Convolutional neural network for automated tooth segmentation on intraoral scans. BMC Oral Health. 2024 Jul 16;24(1):804. doi: 10.1186/s12903-024-04582-2.
10. Li X, Liu W, Kong W, Zhao W, Wang H, Tian D, et al. Prediction of outpatient waiting time: using machine learning in a tertiary children's hospital. Transl Pediatr. 2023 Nov 28;12(11):2030-2043. doi: 10.21037/tp-23-58.
11. Mahesh Batra A, Reche A. A new era of dental care: harnessing artificial intelligence for better diagnosis and treatment. Cureus. 2023 Nov 23;15(11):e49319. doi: 10.7759/cureus.49319.
12. Kapila S, Conley RS, Harrell WE Jr. The current status of cone beam computed tomography imaging in orthodontics. Dentomaxillofac Radiol. 2011 Jan;40(1):24-34. doi: 10.1259/dmfr/12615645.
13. 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.
14. Aguirre RR, Suarez O, Fuentes M, Sanchez-Gonzalez MA. Electronic health record implementation: a review of resources and tools. Cureus. 2019 Sep 13;11(9):e5649. doi: 10.7759/cureus.5649.
15. Hung KF, Yeung AWK, Bornstein MM, Schwendicke F. Personalized dental medicine, artificial intelligence, and their relevance for dentomaxillofacial imaging. Dentomaxillofac Radiol. 2023 Jan 1;52(1):20220335. doi: 10.1259/dmfr.20220335.
16. afala, I.; Bourzgui, F.; Othmani, M.B.; Azmi, M. Automatic classification of malocclusion. Procedia Comput. Sci. 2022, 210, 301–304.
17. Bajwa J, Munir U, Nori A, Williams B. Artificial intelligence in healthcare: transforming the practice of medicine. Future Healthc J. 2021 Jul;8(2):e188-e194. doi: 10.7861/fhj.2021-0095.
18. Khalifa M, Albadawy M. AI in diagnostic imaging: revolutionising accuracy and efficiency. Comput Methods Programs Biomed Updat.2024;5:100146.
doi:10.1016/j.cmpbup.2024.100146
19. Prasad J, Mallikarjunaiah DR, Shetty A, Gandedkar N, Chikkamuniswamy AB, Shivashankar PC. Machine learning predictive model as clinical decision support system in orthodontic treatment planning. Dent J (Basel). 2022 Dec 20;11(1):1. doi: 10.3390/dj11010001..
20. Albalawi F, Alamoud KA. Trends and application of artificial intelligence technology in orthodontic diagnosis and treatment planning—A review. Appl Sci 2022;12(22):11864. DOI: 10.3390/ app122211864.
21. Tomášik, J.; Zsoldos, M.; Oravcová, Ľ.; Lifková, M.; Pavleová, G.; Strunga, M.et al. Ai and face-driven orthodontics: a scoping review of digital advances in diagnosis and treatment planning. AI 2024, 5, 158–176.
22. Surendran A, Daigavane P, Shrivastav S, Kamble R, Sanchla AD, Bharti L, Shinde M. The future of orthodontics: deep learning technologies. Cureus. 2024 Jun 10;16(6):e62045. doi: 10.7759/cureus.62045.
23. Thorat V, Rao P, Joshi N, Talreja P, Shetty AR. Role of artificial intelligence (ai) in patient education and communication in dentistry. Cureus. 2024 May 7;16(5):e59799. doi: 10.7759/cureus.59799.
24. 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 (Basel). 2021 Dec 7;9(12):1695. doi: 10.3390/healthcare9121695.
25. Cunha TMAD, Barbosa IDS, Palma KK. Orthodontic digital workflow: devices and clinical applications. Dental Press J Orthod. 2021 Dec 15;26(6):e21spe6. doi: 10.1590/2177-6709.26.6.e21spe6.
26. Maleki Varnosfaderani S, Forouzanfar M. The role of ai in hospitals and clinics: transforming healthcare in the 21st century. Bioengineering (Basel). 2024 Mar 29;11(4):337. doi: 10.3390/bioengineering11040337.
27. Kumar A, Mani V, Jain V, Gupta H, Venkatesh VG. Managing healthcare supply chain through artificial intelligence (AI): A study of critical success factors. Comput Ind Eng. 2023 Jan;175:108815. doi: 10.1016/j.cie.2022.108815.
28. Dhopte A, Bagde H. Smart Smile: Revolutionizing Dentistry With Artificial Intelligence. Cureus. 2023 Jun 30;15(6):e41227. doi: 10.7759/cureus.41227. PMID: 37529520; PMCID: PMC10387377.
29. Lämmermann L, Hofmann P, Urbach N. Managing artificial intelligence applications in healthcare: Promoting information processing among stakeholders. Int J Inf Manage. 2024 Apr;75:102728. doi: 10.1016/j.ijinfomgt.2023.102728.
30. Woo H., Jha N., Kim Y.-J., Sung S.-J. Evaluating the accuracy of automated orthodontic digital setup models. Semin. Orthod. 2023;29:60–67.
31. Murdoch B. Privacy and artificial intelligence: challenges for protecting health information in a new era. BMC Med Ethics. 2021 Sep 15;22(1):122. doi: 10.1186/s12910-021-00687-3.
32. Williamson, S.M.; Prybutok, V. Balancing privacy and progress: A review of privacy challenges, systemic oversight, and patient perceptions in ai-driven healthcare. Appl. Sci. 2024, 14, 675.
33. Yadav N, Pandey S, Gupta A, Dudani P, Gupta S, Rangarajan K. Data privacy in healthcare: in the era of artificial intelligence. Indian Dermatol Online J. 2023 Oct 27;14(6):788-792. doi: 10.4103/idoj.idoj_543_23.
34. Chiruvella V, Guddati AK. Ethical issues in patient data ownership. Interact J Med Res. 2021 May 21;10(2):e22269. doi: 10.2196/22269.
35. Ferrara E. Fairness and bias in artificial intelligence: a brief survey of sources, impacts, and mitigation strategies. Sci. 2024;6(1):3.
36. Norori N, Hu Q, Aellen FM, Faraci FD, Tzovara A. Addressing bias in big data and AI for health care: A call for open science. Patterns (N Y). 2021 Oct 8;2(10):100347. doi: 10.1016/j.patter.2021.100347.
37. Obermeyer Z, Powers B, Vogeli C, Mullainathan S. Dissecting racial bias in an algorithm
used to manage the health of populations.Science.2019;366:447–453. doi: 10.1126/science.aax2342.
38. Al-Antari MA. Artificial intelligence for medical diagnostics-existing and future ai technology. Diagnostics (Basel). 2023 Feb 12;13(4):688. doi: 10.3390/diagnostics13040688.
39. Auconi P, Gili T, Capuani S, Saccucci M, Caldarelli G, Polimeni A et al. The validity of machine learning procedures in orthodontics: what is still missing? J Pers Med. 2022 Jun 11;12(6):957. doi: 10.3390/jpm12060957.
40. Wang F., Preininger A. AI in health: state of the art, challenges, and future directions. Yearb. Med. Inform. 2019;28:16–26. doi: 10.1055/s-0039-1677908.
41. Naik N, Hameed BMZ, Shetty DK, Swain D, Shah M, Paul R, et al. Legal and ethical consideration in artificial intelligence in healthcare: who takes responsibility? Front Surg. 2022 Mar 14;9:862322. doi: 10.3389/fsurg.2022.862322.
42. Richardson JP, Smith C, Curtis S, Watson S, Zhu X, Barry B, et al. Patient apprehensions about the use of artificial intelligence in healthcare. NPJ Digit Med. 2021 Sep 21;4(1):140. doi: 10.1038/s41746-021-00509-1.
43. Nagy M, Sisk B. How will artificial intelligence affect patient-clinician relationships? AMA J Ethics. 2020 May 1;22(5):E395-400. doi: 10.1001/amajethics.2020.395.
44. US FDA. Artificial Intelligence and Machine Learning (AI/ML)-Enabled Medical Devices. 2023. https://www.fda.gov/medical-devices/software-medical-device-samd/artificial-intelligence-and-machine-learning-aiml-enabled-medical-devices.
45. Larson DB, Harvey H, Rubin DL, Irani N, Tse JR, Langlotz CP. Regulatory frameworks for development and evaluation of artificial intelligence-based diagnostic Imaging Algorithms: Summary and Recommendations. J Am Coll Radiol. 2021 Mar;18(3 Pt A):413-424. doi: 10.1016/j.jacr.2020.09.060.
46. Geneva: World: Health Organization; 2023. Regulatory considerations on artificial intelligence for health. License: CC BY-NC-SA 3.0 IGO. https://iris.who.int/handle/10665/373421, accessed 30 July 2024.
47. Duggal I, Tripathi T. Ethical principles in dental healthcare: Relevance in the current technological era of artificial intelligence. J Oral Biol Craniofac Res. 2024 May-Jun;14(3):317-321. doi: 10.1016/j.jobcr.2024.04.003.
48. Gandedkar NH, Wong MT, Darendeliler MA: Role of virtual reality (vr), augmented reality (ar) and artificial intelligence (ai) in tertiary education and research of orthodontics: an insight. Elsevier;2021;69–77.
49. Moussa R, Alghazaly A, Althagafi N, Eshky R, Borzangy S. Effectiveness of virtual reality and interactive simulators on dental education outcomes: systematic review. Eur J Dent. 2022 Feb;16(1):14-31. doi: 10.1055/s-0041-1731837.
50. Farronato, M., Maspero, C., Lanteri, V. et al. Current state of the art in the use of augmented reality in dentistry: a systematic review of the literature. BMC Oral Health. 2019 July; 19, 135 . https://doi.org/10.1186/s12903-019-0808-3.
51. Strunga M, Urban R, Surovková J, Thurzo A. Artificial intelligence systems assisting in the assessment of the course and retention of orthodontic treatment. Healthcare (Basel). 2023 Feb 25;11(5):683. doi: 10.3390/healthcare11050683.
52. Javaid M, Haleem A, Pratap Singh R, Suman R, Rab S. Significance of machine learning in healthcare: features, pillars and applications. Int J Intell Netw. 2022;3:58–73.
Published
2024-12-02
How to Cite
Nellikottu Soman, S., Thommancheri, N., Mankadavathu Puthenveetil, S., Mukundan, A., Surendran, N., & Akhil S. (2024). Shaping smiles with AI: a comprehensive review of artificial intelligence in orthodontics. UNIVERSITY JOURNAL OF DENTAL SCIENCES, 10(4). https://doi.org/10.21276/ujds.2024.10.4.16
Section
Orthodontics and Dentofacial Orthopedics
