Тюмень, Тюменская область, Россия
Тюмень, Тюменская область, Россия
Тюмень, Тюменская область, Россия
Тюмень, Тюменская область, Россия
Тюмень, Тюменская область, Россия
Тюмень, Тюменская область, Россия
УДК 616.31 Стоматология. Заболевания ротовой полости и зубов
Актуальность. Использование искусственного интеллекта (ИИ) в ортодонтии стало важным технологическим достижением в ортодонтии. Нейросетевые алгоритмы успешно автоматизируют комплексную диагностику, эффективно сегментируют костные структуры, прогнозируют результаты лечения. В ортогнатической хирургии ИИ не только определяет показания к операциям, но и предоставляет точное их 3D-планирование. Визуализация результатов формирует новую парадигму взаимодействия с пациентами. Предмет. Предметом исследования являются алгоритмы искусственного интеллекта для автоматизации диагностики, планирования ортодонтического лечения и прогнозирования его результатов. Цель. Провести анализ литературы, посвященной применению технологий искусственного интеллекта в ортодонтической практике, для определения ключевых векторов цифровой трансформации. Материалы и методы. Проведен обзор литературы с 2019 по 2025 гг., посвященной применению искусственного интеллекта в ортодонтии на основе анализа баз данных eLIBRARY, Scopus, Google Scholar, PubMed/MEDLINE, ResearchGate и MDPI. В результате анализа было отобрано и детально изучено 110 научных исследований. Результаты. Внедрение нейросетевых моделей обеспечило заметный прогресс в цифровой трансформации ортодонтии. ИИ помогает определять и классифицировать зубочелюстные аномалии по фотографиям, устанавливать стадии скелетного возраста, проводить комплексную диагностику включая выявление причин аномалий, цефалометрический анализ, 3D-моделирование, оценку состояния височно-нижнечелюстного сустава (ВНЧС). Эти системы оптимизируют работу клиницистов: ускоряют анализ данных, предоставляют «второе мнение» в сложных случаях, минимизируют диагностические ошибки и создают наглядные результаты лечения. Выводы 1. Современные технологии открывают новые возможности для повышения качества оказания ортодонтической помощи. 2. Однако для успешной интеграции ИИ необходимо решить системные проблемы: улучшение качества входных данных, предотвращение переобучения моделей и проведение полноценной клинической валидации алгоритмов. 3. На текущем этапе происходит переход от экспериментальных разработок к практическому применению, где ведущая роль сохраняется за врачом, а ИИ служит инструментом поддержки принятия решений.
искусственный интеллект, машинное обучение, нейросети, ортодонтия, ортодонтическое лечение, автоматизация
1. Аюпова И.О., Морина А.В. Цифровые методы анализа в ортодонтии. В: Аввакумова Н.П. ред. Синтез наук как основа развития медицинских знаний. Сборник материалов I Межвузовской научно-практической конференции с международным участием; Самара; 18 декабря 2020 года. Самара: Самарский государственный медицинский университет; 2020. С. 482-486. [Ayupova I., Morina A. Digital methods of analysis in orthodontics. In: Avvakumova N.P. ed. Synthesis of Sciences as a Basis for the Development of Medical Knowledge. Proceedings of the First Interuniversity Scientific and Practical Conference with International Participation; Samara; December 18, 2020. Samara: Samara State Medical University; 2020. pp. 482-486. (In Russ.)]. https://www.elibrary.ru/item.asp?edn=hbuqrx
2. Добратулин К.С., Аюпова И.О., Гайдель А.В. Нейронные сети в диагностике и планировании ортодонтического лечения. В: Аввакумова Н.П. ред. Синтез наук как основа развития медицинских знаний. Сборник материалов I Межвузовской научно-практической конференции с международным участием; Самара; 18 декабря 2020 года. Самара: Самарский государственный медицинский университет; 2020. С. 500-505. [Dobratulin K., Aupova I., Gaidel A. Neural Networks in the Diagnosis and Planning of Orthodontic Treatment. In: Avvakumova N.P. ed. Synthesis of Sciences as a Basis for the Development of Medical Knowledge. Proceedings of the First Interuniversity Scientific and Practical Conference with International Participation; Samara; December 18, 2020. Samara: Samara State Medical University; 2020. pp. 500-505. (In Russ.)]. https://www.elibrary.ru/item.asp?id=44571699
3. Кабардова К.Х., Килова М.А., Дешев А.В. Цифровая трансформация в 3D-цефалометрии. В: Перспектива-2024. Материалы Международной научной конференции студентов, аспирантов и молодых ученых; Нальчик; 26–29 апреля 2024 года. Т. 3. Нальчик: Кабардино-Балкарский государственный университет им. Х.М. Бербекова; 2024. С. 3-5. [Kabardova K.H., Kilova M.A., Deshev A.V. Digital transformation in 3D cephalometry. In: Perspektiva-2024. Proceedings of the International Scientific Conference of Students, Postgraduates and Young Scientists; Nalchik; April 26-29, 2024. Vol. 3. Nalchik: Kabardino-Balkarian State University named after H.M. Berbekov; 2024. pp. 3-5. (In Russ.)]. https://elibrary.ru/item.asp?id=77259865
4. Колсанов А.В., Попов Н.В., Аюпова И.О., Добратулин К.С., Гайдель А.В., Ивлева А.И. Разработка программного комплекса диагностики зубочелюстных аномалий с применением нейронных сетей. В: Информационные технологии и нанотехнологии (ИТНТ-2021). Сборник трудов по материалам VII Международной конференции и молодежной школы; Самара; 20–24 сентября 2021 года. Том 3. Самара: Самарский национальный исследовательский университет имени академика С.П. Королева; 2021. Статья 34572. [Kolsanov A.V., Popov N.V., Ayupova I.O., Dobratulin K.S., Gaidel' A.V., Ivleva A.I. Development of a Software Complex for Diagnosing Dentofacial Anomalies Using Neural Networks. In: Information Technology and Nanotechnology (ITNT-2021). Proceedings of the VII International Conference and Youth School; Samara; September 20-24, 2021. Vol. 3. Samara: Samara National Research University named after academician S.P. Korolev; 2021. Article 34572. (In Russ.)]. https://elibrary.ru/bhssoy
5. Оборотистов Н.Ю., Мураев А.А., Сенько Д.А. Инновационная онлайн-платформа ViSurgery для расчета телерентгенограмм с использованием искусственного интеллекта. Ортодонтия. 2022;(4):15-20. [Oborotistov N.Yu., Muraev A.A., Senko D.A. ViSurgery, a new online platform for 2d-cephalometric analysis using artificial intelligence. Ortodontiâ. 2022;(4):15-20. (In Russ.)]. https://elibrary.ru/item.asp?id=50253627
6. Оборотистов Н.Ю., Мураев А.А. Применение искусственного интеллекта в стоматологии. Российская стоматология. 2023;16(4):70-71. [Oborotistov N.Yu., Muraev A.A. Application of Artificial Intelligence in Dentistry Russian Journal of Stomatology. 2023;16(4):70-71. (In Russ.)]. https://www.mediasphera.ru/issues/rossijskaya-stomatologiya/2023/4/1207264062023041048
7. Рахматалиев Д.И., Подуданский П.А., Зрюмов П.А. Разработка программного обеспечения для определения цефалометрических точек на телерентгенограммах. В: Современные цифровые технологии. Материалы III Всероссийской научно-практической конференции; Барнаул; 03 июня 2024 года. Барнаул: Алтайский государственный технический университет им. И.И. Ползунова; 2024. С. 265-268. [Rakhmataliev D.I., Podudanskiy P.A., Zryumov P.A. Development of Software for Determining Cephalometric Points on Lateral Cephalograms. In: Modern Digital Technologies. Materials of the III All-Russian Scientific and Practical Conference; Barnaul; June 03, 2024. Barnaul: Altai State Technical University named after I.I. Polzunov; 2024. pp. 265-268. (In Russ.)]. https://elibrary.ru/item.asp?id=68573399
8. Рахимбердиев Р.А., Сайфулаева А.А. Использование искусственного интеллекта для прогноза результатов лечения в ортодонтии. Journal Of New Century Innovations. 2025;78(4):101-105. [Rakhimberdiev R.A., Sayfulaeva A.A. Use of Artificial Intelligence to Predict Treatment Outcomes in Orthodontics. Journal of New Century Innovations. 2025;78(4):101-105. (In Russ.)]. https://scientific-jl.com/new/article/view/20162
9. Рузиев Ш., Нигматов Р. Болаларда прогнатик прикусни ортодонтик даволашни такомиллаштириш ва унинг самарадорлигини сунъий интеллект ердамида баҳолаш (Услубий тавсиянома). In Library. 2025;1(1):2–19. [Ruziyev Sh.D., Nigmatov R.N. Improving orthodontic treatment of prognathic occlusion in children andevaluating its effectiveness using artificial intelligence. In Library. 2025;1(1):2–19. (In Uzbek.)]. https://inlibrary.uz/index.php/archive/article/view/130050
10. Рыбаков Александр. Оптимизация ортодонтического лечения на основе нейронных сетей, анализа конечными элементами и цифровых карт слизистой полости рта: диссертация на соискание ученой степени кандидата медицинских наук. Санкт-Петербург; 2024. 235 с. [Rybakov Aleksandr. Orthodontic treatment optimization based on neural networks, finiteelement analysis and oral mucosa digital maps: Dissertation for the degree of candidate of medical sciences. Saint-Petersburg; 2024. 235 p. (In Russ.)]. https://disser.spbu.ru/files/2024/disser_rybakov_aleksandr.pdf
11. Салеев Р.А., Зайнутдинова А.Р., Мустакимова Р.Ф. Использование искусственного интелекта Diagnocat в современной стоматологии. В: Актуальные вопросы стоматологии. Сборник научных трудов, посвященный основателю кафедры ортопедической стоматологии КГМУ, профессору Исаак Михайловичу Оксману. Казань: Казанский государственный медицинский университет; 2025. С. 501-506. [Saleev R.A., Zainutdinova A.R., Mustakimova R.F. The Use of Artificial Intelligence Diagnocat in Modern Dentistry. In: Current Issues in Dentistry. Collection of Scientific Works Dedicated to the Founder of the Department of Orthopedic Dentistry of KSMU, Professor Isaak Mikhailovich Oksman. Kazan: Kazan State Medical University; 2025. pp. 501-506. (In Russ.)]. https://elibrary.ru/item.asp?id=82414727
12. Симакова А.А., Горбатова М.А., Русанов Д.С., Карякин А.А., Гржибовский А.М. Применение искусственных нейронных сетей в практике врача-ортодонта: обзор российских исследований за период 2013-2023. Вестник новых медицинских технологий. Электронное издание. 2024;18(4):24-35. [Simakova A.A., Gorbatova M.A., Rusanov D.S., Karyakin A.A., Grzhibovsky A.M. Application of artificial neural networks in orthodontic practice: a review of russian studies for the period 2013-2023. Journal of New Medical Technologies. 2024;18(4):24-35. (In Russ.)]. https://elibrary.ru/item.asp?id=69158660
13. 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 research international. 2021;2021:9954615. https://doi.org/10.1155/2021/9954615
14. Akay G., Akcayol M.A., Özdem K., Güngör K. Deep convolutional neural network-the evaluation of cervical vertebrae maturation. Oral radiology. 2023;39(4):629-638. https://doi.org/10.1007/s11282-023-00678-7
15. Alam M.K., Alanazi D.S.A., Alruwaili S.R.F., Alderaan R.A.I. Assessment of AI models in predicting treatment outcomes in orthodontics. Journal of Pharmacy and Bioallied Sciences. 2024;16(Suppl 1):S540-S542. https://doi.org/10.4103/jpbs.jpbs_852_23
16. Albalawi F., Abalkhail K.A. Trends and Application of Artificial Intelligence Technology in Orthodontic Diagnosis and Treatment Planning—A Review. Applied Sciences. 2022;12(22):11864. https://doi.org/10.3390/app122211864
17. Almășan O., Leucuța D.C., Hedeșiu M., Mureșanu S., Popa Ș.L. Temporomandibular joint osteoarthritis diagnosis employing artificial intelligence: systematic review and meta-analysis. Journal of Clinical Medicine. 2023;12(3):942. https://doi.org/10.3390/jcm12030942
18. Amasya H., Yildirim D., Aydogan T., Kemaloglu N., Orhan K. Cervical vertebral maturation assessment on lateral cephalometric radiographs using artificial intelligence: comparison of machine learning classifier models. Dento maxillo facial radiology. 2020;49(5):20190441. https://doi.org/10.1259/dmfr.20190441
19. Arsiwala-Scheppach L.T., Chaurasia A., Müller A., Krois J., Schwendicke F. Machine Learning in Dentistry: A Scoping Review. Journal of clinical medicine. 2023;12(3):937. https://doi.org/10.3390/jcm12030937
20. Atici S.F., Ansari R., Allareddy V., Suhaym O., Cetin A.E., Elnagar M.H. AggregateNet: A deep learning model for automated classification of cervical vertebrae maturation stages. Orthodontics & craniofacial research. 2023;26(Suppl 1):111-117. https://doi.org/10.1111/ocr.12644
21. Ayidh Alqahtani K., Jacobs R., Smolders A., Van Gerven A., Willems H., Shujaat S. et al. Deep convolutional neural network-based automated segmentation and classification of teeth with orthodontic brackets on cone-beam computed-tomographic images: a validation study. European journal of orthodontics. 2023;45(2):169-174. https://doi.org/10.1093/ejo/cjac047
22. Bao H., Zhang K., Yu C., Li H., Cao D., Shu H. et al. Evaluating the accuracy of automated cephalometric analysis based on artificial intelligence. BMC Oral Health. 2023;23(1):191. https://doi.org/10.1186/s12903-023-02881-8
23. Bianchi J., Ruellas A., Prieto J.C., Li T., Soroushmehr R., Najarian K. et al. Decision support systems in temporomandibular joint osteoarthritis: a review of data science and artificial intelligence applications. Seminars in orthodontics. 2021;27(2):78-86. https://doi.org/10.1053/j.sodo.2021.05.004
24. Bianchi J., de Oliveira Ruellas A.C., Gonçalves J.R., Paniagua B., Prieto J.C., Styner M. et al. Osteoarthritis of the temporomandibular joint can be diagnosed earlier using biomarkers and machine learning. Scientific reports. 2020;10(1):8012. https://doi.org/10.1038/s41598-020-64942-0
25. Bulatova G., Kusnoto B., Grace V., Tsay T.P., Avenetti D.M., Sanchez F.J.C. Assessment of automatic cephalometric landmark identification using artificial intelligence. Orthodontics & craniofacial research. 2021;24(Suppl 2):37-42. https://doi.org/10.1111/ocr.12542
26. 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(5):1856. https://doi.org/10.3390/s21051856
27. Cassi D., Battistoni G., Magnifico M., Di Blasio C., Pedrazzi G., Di Blasio A. Three-dimensional evaluation of facial asymmetry in patients with hemifacial microsomia using stereophotogrammetry. 2019;47(1):179-184. https://doi.org/10.1016/j.jcms.2018.11.011
28. Chaiprasittikul N., Thanathornwong B., Pornprasertsuk-Damrongsri S., Raocharernporn S., Maponthong S., Manopatanakul S. Application of a Multi-Layer Perceptron in Preoperative Screening for Orthognathic Surgery. Healthcare Informatics Research. 2023;29(1):16-22. https://doi.org/10.4258/hir.2023.29.1.16
29. Choi H.I., Jung S.K., Baek S.H., Lim W.H., Ahn S.J., Yang I.H. et al. Artificial intelligent model with neural network machine learning for the diagnosis of orthognathic surgery. Journal of Craniofacial Surgery. 2019;30(7):1986-1989. https://doi.org/10.1097/scs.0000000000005650
30. Chung M., Lee J., Song W., Song Y., Yang I.H., Lee J. et al. Automatic registration between dental cone-beam CT and scanned surface via deep pose regression neural networks and clustered similarities. IEEE Transactions on Medical Imaging. 2020;39(12):3900-3909. https://doi.org/10.1109/tmi.2020.3007520
31. 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. 2022;52(2):102-111. https://doi.org/10.4041/kjod.2022.52.2.102
32. D'Ettorre G., Farronato M., Candida E., Quinzi V., Grippaudo C. A comparison between stereophotogrammetry and smartphone structured light technology for three-dimensional face scanning. The Angle orthodontist. 2022;92(3):358-363. https://doi.org/10.2319/040921-290.1
33. Ding H., Wu J., Zhao W., Matinlinna J.P., Burrow M.F., Tsoi J.K.H. Artificial intelligence in dentistry—A review. Frontiers in Dental Medicine. 2023;4:1085251. https://doi.org/10.3389/fdmed.2023.1085251
34. Dipalma G., Inchingolo A.D., Inchingolo A.M., Piras F., Carpentiere V., Garofoli G. et al. Artificial Intelligence and Its Clinical Applications in Orthodontics: A Systematic Review. Diagnostics. 2023;13(24):3677. https://doi.org/10.3390/diagnostics13243677
35. Dong H., Abdulsalam Abdullah R. Artificial intelligence prediction model for the relationship between obstructive sleep Apnea severity and maxillofacial developmental disorders in children: a prospective cohort study. Future Technology. 2025;4(3):85-96. https://fupubco.com/futech/article/view/364
36. Duran G.S., Gökmen Ş., Topsakal K.G., Görgülü S. Evaluation of the accuracy of fully automatic cephalometric analysis software with artificial intelligence algorithm. Orthodontics & Craniofacial Research. 2023;26(3):481-490. https://doi.org/10.1111/ocr.12633
37. Etemad L., Wu T.H., Heiner P., Liu J., Lee S., Chao W. et al. Machine learning from clinical data sets of a contemporary decision for orthodontic tooth extraction. Orthodontics & Craniofacial Research. 2021;24(Suppl 2):193-200. https://doi.org/10.1111/ocr.12502
38. Evangelista K., de Freitas Silva B.S., Yamamoto-Silva F.P., Valladares-Neto J., Silva M.A.G., Cevidanes L.H.S. et al. Accuracy of artificial intelligence for tooth extraction decision-making in orthodontics: a systematic review and meta-analysis. Clinical Oral Investigations. 2022;26(12):6893-6905. https://doi.org/10.1007/s00784-022-04742-0
39. Faber J., Faber C., Faber P. Artificial intelligence in orthodontics. APOS Trends in Orthodontics. 2019;9(4):201–205. https://doi.org/10.25259/APOS_123_2019
40. Ferlito T., Hsiou D., Hargett K., Herzog C., Bachour P., Katebi N. et al. Assessment of artificial intelligence-based remote monitoring of clear aligner therapy: A prospective study. American journal of orthodontics and dentofacial orthopedics. 2023;164(2):194-200. https://doi.org/10.1016/j.ajodo.2022.11.020
41. Gala K.M. Ethical and Legal Considerations in AI-Driven Health Cybersecurity. International Journal of Scientific Research in Computer Science, Engineering and Information Technology. 2024;10(5):682-90. https://doi.org/10.32628/CSEIT241051059
42. 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. Journal of pharmacy & bioallied sciences. 2024;16(Suppl 2):S1798-S1800. https://doi.org/10.4103/jpbs.jpbs_1147_23
43. Alqahtani H. Evaluation of an online website-based platform for cephalometric analysis. Journal of Stomatology, Oral and Maxillofacial Surgery. 2020;121(1):53-57. https://doi.org/10.1016/j.jormas.2019.04.017
44. 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. American Journal of Orthodontics and Dentofacial Orthopedics. 2021;160(2):170-192.e4. https://doi.org/10.1016/j.ajodo.2021.02.013
45. Hu X., Zhao Y., Yang C. Evaluation of root position during orthodontic treatment via multiple intraoral scans with automated registration technology. American Journal of Orthodontics and Dentofacial Orthopedics. 2023;164(2):285-292. https://doi.org/10.1016/j.ajodo.2023.04.012
46. Hwang H.W., Moon J.H., Kim M.G., Donatelli R.E., Lee S.J. Evaluation of automated cephalometric analysis based on the latest deep learning method. 2021;91(3):329-335. https://doi.org/10.2319/021220-100.1
47. Ito S., Mine Y., Yoshimi Y., Takeda S., Tanaka A., Onishi A. et al. Automated segmentation of articular disc of the temporomandibular joint on magnetic resonance images using deep learning. Scientific Reports. 2022;12(1):221. https://doi.org/10.1038/s41598-021-04354-w
48. Jha N., Lee K.S., Kim Y.J. Diagnosis of temporomandibular disorders using artificial intelligence technologies: A systematic review and meta-analysis. PLoS One. 2022;17(8):e0272715. https://doi.org/10.1371/journal.pone.0272715
49. Jihed M., Dallel I., Tobji S., Ben Amor A. The impact of artificial intelligence on contemporary orthodontic treatment planning-a systematic review and meta-analysis. Scholars Journal of Dental Sciences. 2022;9(5):70-87. https://doi.org/10.36347/sjds.2022.v09i05.001
50. 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. 2016;149(1):127-33. https://doi.org/10.1016/j.ajodo.2015.07.030
51. Kazimierczak N., Kazimierczak W., Serafin Z., Nowicki P., Lemanowicz A., Nadolska K. et al. Correlation analysis of nasal septum deviation and results of AI-driven automated 3D cephalometric analysis. Journal of Clinical Medicine. 2023;12(20):6621. https://doi.org/10.3390/jcm12206621
52. Khanagar S.B., Al-Ehaideb A., Vishwanathaiah S., Maganur P.C., Patil S., Naik S. et al. Scope and performance of artificial intelligence technology in orthodontic diagnosis, treatment planning, and clinical decision-making-a systematic review. Journal of dental sciences. 2021;16(1):482-492. https://doi.org/10.1016/j.jds.2020.05.022
53. Kim H., Kim C.S., Lee J.M., Lee J.J., Lee J., Kim J.S. et al. Prediction of Fishman’s skeletal maturity indicators using artificial intelligence. Scientific Reports. 2023;13(1):5870. https://doi.org/10.1038/s41598-023-33058-6
54. Kim H., Shim E., Park J., Kim Y.J., Lee U., Kim Y. Web-based fully automated cephalometric analysis by deep learning. Computer methods and programs in biomedicine. 2020;194:105513. https://doi.org/10.1016/j.cmpb.2020.105513
55. Kim H.J., Kim K.D., Kim D.H. Deep convolutional neural network-based skeletal classification of cephalometric image compared with automated-tracing software. Scientific reports. 2022;12(1):11659. https://doi.org/10.1038/s41598-022-15856-6
56. Kim M.J., Liu Y., Oh S.H., Ahn H.W., Kim S.H., Nelson G. Evaluation of a multi-stage convolutional neural network-based fully automated landmark identification system using cone-beam computed tomography-synthesized posteroanterior cephalometric images. Korean Journal of Orthodontics. 2021;51(2):77-85. https://doi.org/10.4041/kjod.2021.51.2.77
57. Kim Y.H., Park J.B., Chang M.S., Ryu J.J., Lim W.H., Jung S.K. Influence of the Depth of the Convolutional Neural Networks on an Artificial Intelligence Model for Diagnosis of Orthognathic Surgery. Journal of personalized medicine. 2021;11(5):356. https://doi.org/10.3390/jpm11050356
58. Ko C., Tanikawa C., Wu T., Pastewait M., Jackson C.B., Kwon J.J. et al. Machine learning in orthodontics: application review. Progress in Orthodontics. 2021;22(1):18. https://doi.org/10.1186/s40510-021-00361-9
59. Kök H., Acilar A.M., İzgi M.S. Usage and comparison of artificial intelligence algorithms for determination of growth and development by cervical vertebrae stages in orthodontics. Progress in Orthodontics. 2019;20(1):41. https://doi.org/10.1186/s40510-019-0295-8
60. Konstantonis D., Anthopoulou C., Makou M. Extraction decision and identification of treatment predictors in Class I malocclusions. Progress in Orthodontics. 2013;14:47. https://doi.org/10.1186/2196-1042-14-47
61. Kunz F., Stellzig-Eisenhauer A., Zeman F., Boldt J. Artificial intelligence in orthodontics: Evaluation of a fully automated cephalometric analysis using a customized convolutional neural network. Journal of Orofacial Orthopedics. 2020;81(1):52-68. https://doi.org/10.1007/s00056-019-00203-8
62. Le V.N.T., Kang J., Oh I.S., Kim J.H., Yang Y.M., Lee D.W. Effectiveness of human–Artificial intelligence collaboration in cephalometric landmark detection. Journal of Personalized Medicine. 2022;12(3):387. https://doi.org/10.3390/jpm12030387
63. Lee H., Ahmad S., Frazier M., Dundar M.M., Turkkahraman H. A novel machine learning model for class III surgery decision. Journal of Orofacial Orthopedics. 2024;85(4):239-249. https://doi.org/10.1007/s00056-022-00421-7
64. Lee J.H., Yu H.J., Kim M.J., Kim J.W., Choi J. Automated cephalometric landmark detection with confidence regions using Bayesian convolutional neural networks. BMC Oral Health. 2020;20(1):270. https://doi.org/10.1186/s12903-020-01256-7
65. Lee S.C., Hwang H.S., Lee K.C. Accuracy of deep learning-based integrated tooth models by merging intraoral scans and CBCT scans for 3D evaluation of root position during orthodontic treatment. Progress in Orthodontics. 2022;23(1):15. https://doi.org/10.1186/s40510-022-00410-x
66. Li P., Kong D., Tang T., Su D., Yang P., Wang H. et al. Orthodontic Treatment Planning based on Artificial Neural Networks. Scientific Reports. 2019;9(1):2037. https://doi.org/10.1038/s41598-018-38439-w
67. Li S., Guo Z., Lin J., Ying S. Artificial intelligence for classifying and archiving orthodontic images. BioMed Research International. 2022;2022:1473977. https://doi.org/10.1155/2022/1473977
68. Lin G., Kim P.J., Baek S.H., Kim H.G., Kim S.W., Chung J.H. Early Prediction of the Need for Orthognathic Surgery in Patients With Repaired Unilateral Cleft Lip and Palate Using Machine Learning and Longitudinal Lateral Cephalometric Analysis Data. Journal of Craniofacial Surgery. 2021;32(2):616-620. https://doi.org/10.1097/SCS.0000000000006943
69. Lin H.H., Chiang W.C., Yang C.T., Cheng C.T., Zhang T., Lo L.J. On construction of transfer learning for facial symmetry assessment before and after orthognathic surgery. Computer Methods and Programs in Biomedicine. 2021;200:105928. https://doi.org/10.1016/j.cmpb.2021.105928
70. Liu Z., He X., Wang H., Xiong H., Zhang Y., Wang G. et al. Hierarchical self-supervised learning for 3D tooth segmentation in intra-oral mesh scans. IEEE Transactions on Medical Imaging. 2023;42(2):467-480. https://doi.org/10.1109/TMI.2022.3222388
71. Ma Q., Kobayashi E., Fan B., Nakagawa K., Sakuma I., Masamune K. et al. Automatic 3D landmarking model using patch-based deep neural networks for CT image of oral and maxillofacial surgery. International Journal of Medical Robotics and Computer Assisted Surgery. 2020;16(3):e2093. https://doi.org/10.1002/rcs.2093
72. Mahto R.K., Kafle D., Giri A., Luintel S., Karki A. Evaluation of fully automated cephalometric measurements obtained from web-based artificial intelligence driven platform. BMC Oral Health. 2022;22(1):132. https://doi.org/10.1186/s12903-022-02170-w
73. Miloglu O., Guller M.T., Tosun Z.T. The Use of Artificial Intelligence in Dentistry Practices. Eurasian Journal of Medicine. 2022;54(Suppl 1):34-42. https://doi.org/10.5152/eurasianjmed.2022.22301
74. Moon J.H., Shin H.K., Lee J.M., Cho S.J., Park J.A., Donatelli R.E. et al. Comparison of individualized facial growth prediction models based on the partial least squares and artificial intelligence. The Angle Orthodontist. 2024;94(2):207-215. https://doi.org/10.2319/031723-181.1
75. Muraev A.A., Tsai P., Kibardin I., Oborotistov N., Shirayeva T., Ivanov S. Frontal cephalometric landmarking: humans vs artificial neural networks. International Journal of Computerized Dentistry. 2020;23(2):139–148.
76. Nishimoto S., Sotsuka Y., Kawai K., Ishise H., Kakibuchi M. Personal computer-based cephalometric landmark detection with deep learning, using cephalograms on the internet. Journal of Craniofacial Surgery. 2019;30(1):91-95. https://doi.org/10.1097/SCS.0000000000004901
77. Ozsari S., Güzel M.S., Yılmaz D., Kamburoğlu K. A comprehensive review of artificial intelligence based algorithms regarding temporomandibular joint related diseases. Diagnostics. 2023;13(16):2700. https://doi.org/10.3390/diagnostics13162700
78. Park J.A., Moon J.H., Lee J.M., Cho S.J., Seo B.M., Donatelli R. E. et al. Does artificial intelligence predict orthognathic surgical outcomes better than conventional linear regression methods? The Angle Orthodontist. 2024;94(5):549-556. https://doi.org/10.2319/111423-756.1
79. Park J.H., Kim Y.J., Kim J., Kim J., Kim I.-H., Kim N. et al. Use of artificial intelligence to predict outcomes of nonextraction treatment of Class II malocclusions. Seminars in Orthodontics. 2021;27(2):87-95. https://doi.org/10.1053/j.sodo.2021.05.005
80. Park Y.S., Choi J.H., Kim Y., Choi S.H., Lee J.H., Kim K.H. et al. Deep learning-based prediction of the 3D postorthodontic facial changes. Journal of Dental Research. 2022;101(11):1372-1379. https://doi.org/10.1177/00220345221106676
81. Prados-Privado M., García Villalón J., Martínez-Martínez C.H., Ivorra C., Prados-Frutos J.C. Dental caries diagnosis and detection using neural networks: a systematic review. Journal of Clinical Medicine. 2020;9(11):3579. https://doi.org/10.3390/jcm9113579
82. Prasad J., Mallikarjunaiah D.R., Shetty A., Gandedkar N., Chikkamuniswamy A.B., Shivashankar P.C. Machine Learning Predictive Model as Clinical Decision Support System in Orthodontic Treatment Planning. Dentistry Journal. 2022;11(1):1. https://doi.org/10.3390/dj11010001
83. Ramezanzade S., Laurentiu T., Bakhshandah A., Ibragimov B., Kvist T., Bjørndal L. The efficiency of artificial intelligence methods for finding radiographic features in different endodontic treatments-a systematic review. Acta Odontologica Scandinavica. 2023;81(6):422-435. https://doi.org/10.1080/00016357.2022.2158929
84. Retrouvey J.M. The role of AI and machine learning in contemporary orthodontics. APOS Trends in Orthodontics. 2021;11:74-80. https://doi.org/10.25259/APOS_148_2020
85. Rousseau M., Retrouvey J.M. Machine learning in orthodontics: Automated facial analysis of vertical dimension for increased precision and efficiency. American Journal of Orthodontics and Dentofacial Orthopedics. 2022;161(3):445-450. https://doi.org/10.1016/j.ajodo.2021.03.017
86. Ryu J., Lee Y.S., Mo S.P., Lim K., Jung S.K., Kim T.W. Application of deep learning artificial intelligence technique to the classification of clinical orthodontic photos. BMC Oral Health. 2022;22(1):454. https://doi.org/10.1186/s12903-022-02466-x
87. Ryu J., Kim Y.H., Kim T.W., Jung S.K. Evaluation of artificial intelligence model for crowding categorization and extraction diagnosis using intraoral photographs. Scientific Reports. 2023;13(1):5177. https://doi.org/10.1038/s41598-023-32514-7
88. Sadeghi T.S., Ourang S.A., Sohrabniya F., Sadr S., Shobeiri P., Motamedian S.R. Performance of artificial intelligence on cervical vertebral maturation assessment: a systematic review and meta-analysis. BMC Oral Health. 2025;25(1):187. https://doi.org/10.1186/s12903-025-05482-9
89. Talaat S., Kaboudan A., Talaat W., Kusnoto B., Sanchez F., Elnagar M.H. et al. The validity of an artificial intelligence application for assessment of orthodontic treatment need from clinical images. Seminars in Orthodontics. 2021;27(2):164-171. https://doi.org/10.1053/j.sodo.2021.05.012
90. Schwendicke F., Golla T., Dreher M., Krois J. Convolutional neural networks for dental image diagnostics: A scoping review. Journal of Dentistry. 2019;91:103226. https://doi.org/10.1016/j.jdent.2019.103226
91. Setzer F.C., Shi K.J., Zhang Z., Yan H., Yoon H., Mupparapu M. et al. Artificial intelligence for the computer-aided detection of periapical lesions in cone-beam computed tomographic images. Journal of Endodontics. 2020;46(7):987-993. https://doi.org/10.1016/j.joen.2020.03.025
92. Shin W., Yeom H.G., Lee G.H., Yun J.P., Jeong S.H., Lee J.H. et al. Deep learning based prediction of necessity for orthognathic surgery of skeletal malocclusion using cephalogram in Korean individuals. BMC Oral Health. 2021;21(1):130. https://doi.org/10.1186/s12903-021-01513-3
93. Shojaei H., Augusto V. Constructing Machine Learning Models for Orthodontic Treatment Planning: A Comparison of Different Methods. In: 2022 IEEE International Conference on Big Data (Big Data); Osaka, Japan; December 17-20, 2022. pp. 2790-2799. https://doi.org/10.1109/BigData55660.2022.10021045
94. Snider V., Homsi K., Kusnoto B., Atsawasuwan P., Viana G., Allareddy V. et al. Effectiveness of AI-driven remote monitoring technology in improving oral hygiene during orthodontic treatment. Orthodontics & Craniofacial Research. 2023;26(Suppl 1):102-110. https://doi.org/10.1111/ocr.12666
95. 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(5):683. https://doi.org/10.3390/healthcare11050683
96. Subramanian A.K., Chen Y., Almalki A., Sivamurthy G., Kafle D. Cephalometric Analysis in Orthodontics Using Artificial Intelligence-A Comprehensive Review. BioMed Research International. 2022;2022:1880113. https://doi.org/10.1155/2022/1880113
97. Suhail Y., Upadhyay M., Chhibber A., Kshitiz. Machine Learning for the Diagnosis of Orthodontic Extractions: A Computational Analysis Using Ensemble Learning. Bioengineering. 2020;7(2):55. https://doi.org/10.3390/bioengineering7020055
98. Surendran A., Daigavane P., Shrivastav S., Kamble R., Sanchla A.D., Bharti L. et al. The Future of Orthodontics: Deep Learning Technologies. Cureus. 2024;16(6):e62045. https://doi.org/10.7759/cureus.62045
99. Tanikawa C., Yamashiro T. Development of novel artificial intelligence systems to predict facial morphology after orthognathic surgery and orthodontic treatment in Japanese patients. Scientific Reports. 2021;11(1):15853. https://doi.org/10.1038/s41598-021-95002-w
100. Volovic J., Badirli S., Ahmad S., Leavitt L., Mason T., Bhamidipalli S.S. et al. A novel machine learning model for predicting orthodontic treatment duration. Diagnostics. 2023;13(17):2740. https://doi.org/10.3390/diagnostics13172740
101. Woo H., Jha N., Kim Y.J., Sung S.J. Evaluating the accuracy of automated orthodontic digital setup models. Seminars in Orthodontics. 2023;29(1):60-67. https://doi.org/10.1053/j.sodo.2022.12.010
102. Wood T., Anigbo J.O., Eckert G., Stewart K.T., Dundar M.M., Turkkahraman H. Prediction of the Post-Pubertal Mandibular Length and Y Axis of Growth by Using Various Machine Learning Techniques: A Retrospective Longitudinal Study. Diagnostics. 2023;13(9):1553. https://doi.org/10.3390/diagnostics13091553
103. Xu L., Chen J., Qiu K., Yang F., Wu W. Artificial intelligence for detecting temporomandibular joint osteoarthritis using radiographic image data: A systematic review and meta-analysis of diagnostic test accuracy. PLOS ONE. 2023;18(7):e0288631. https://doi.org/10.1371/journal.pone.0288631
104. Yao J., Zeng W., He T., Zhou S., Zhang Y., Guo J. et al. Automatic localization of cephalometric landmarks based on convolutional neural network. American Journal of Orthodontics and Dentofacial Orthopedics. 2022;161(3):e250-e259. https://doi.org/10.1016/j.ajodo.2021.09.012
105. Yassir Y.A., Salman A.R., Nabbat S.A. The accuracy and reliability of WebCeph for cephalometric analysis. Journal of Taibah University Medical Sciences. 2022;17(1):57-66. https://doi.org/10.1016/j.jtumed.2021.08.010
106. Ye H., Cheng Z., Ungvijanpunya N., Chen W., Cao L., Gou Y. Is automatic cephalometric software using artificial intelligence better than orthodontist experts in landmark identification? BMC Oral Health. 2023;23(1):467. https://doi.org/10.1186/s12903-023-03188-4
107. Yu H.J., Cho S.R., Kim M.J., Kim W.H., Kim J.W., Choi J. Automated skeletal classification with lateral cephalometry based on artificial intelligence. Journal of Dental Research. 2020;99(3):249-256. https://doi.org/10.1177/0022034520901715
108. Yurdakurban E., Duran G.S., Görgülü S. Evaluation of an automated approach for facial midline detection and asymmetry assessment: A preliminary study. Orthodontics & Craniofacial Research. 2021;24(Suppl 2):84-91. https://doi.org/10.1111/ocr.12539
109. Zeng M., Yan Z., Liu S., Zhou Y., Qiu L. Cascaded convolutional networks for automatic cephalometric landmark detection. Medical Image Analysis. 2021;68:101904. https://doi.org/10.1016/j.media.2020.101904
110. Zhao T., Zhou J., Yan J., Cao L., Cao Y., Hua F. et al. Automated adenoid hypertrophy assessment with lateral cephalometry in children based on artificial intelligence. Diagnostics. 2021;11(8):1386. https://doi.org/10.3390/diagnostics11081386
