Actual problems in dentistry Actual problems in dentistry Проблемы стоматологии 2077-7566 2412-9461 111741 10.18481/2077-7566-2025-21-4-13-26 ЛЕКЦИИ / ЛИТЕРАТУРНЫЕ ОБЗОРЫ LITERATURE REVIEW ЛЕКЦИИ / ЛИТЕРАТУРНЫЕ ОБЗОРЫ DIGITAL TRANSFORMATION IN ORTHODONTICS (LITERATURE REVIEW) ЦИФРОВАЯ ТРАНСФОРМАЦИЯ ОРТОДОНТИИ (ЛИТЕРАТУРНЫЙ ОБЗОР) Иванов Александр Евгеньевич Ivanov Alexandr E. aleksandr-9001@bk.ru Брагин Александр Витальевич Bragin Aleksandr Vitalievich dekanat_stomat@tyumensmu.ru

доктор медицинских наук;

doctor of medical sciences;

 Лебедев Алексей Валерьевич Lebedev Aleksey V. dr_alexlebedev@mail.ru

кандидат медицинских наук;

candidate of medical sciences;

 Куратова Луиза Минизакиевна Kuratova Luiza M. luizonchik@mail.ru

кандидат медицинских наук;

candidate of medical sciences;

 Куратов Илья Александрович Kuratov Ilia A. alliance-med@mail.ru Биктимиров Айдар Саитович Biktimirov Aidar S. doc.biktimirov@yandex.ru Тюменского государственного медицинского университета Тюмень Россия Tyumen State Medical University Tyumen Russian Federation Тюменский государственный медицинский университет Tyumen State Medical University Тюменский государственный медицинский университет Тюмень Россия Тюменский государственный медицинский университет Тюмень Russian Federation Тюменский государственный медицинский университет Тюмень Россия Tyumen State Medical University Tyumen Russian Federation Тюменский государственный медицинский университет Тюмень Россия Tyumen State Medical University Tyumen Russian Federation Тюменский государственный медицинский университет Тюмень Россия Tyumen State Medical University Tyumen Russian Federation 29 01 2026 29 01 2026 21 4 13 26 07 01 2026 https://dental-press.ru/en/nauka/article/111741/view

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.

Relevance. Artificial intelligence (AI) has become a key technological advancement in orthodontics. Neural network algorithms successfully automate comprehensive diagnostics, effectively segment bone structures, and predict treatment outcomes based on individual patient characteristics. In orthognathic surgery, AI not only determines the indications for surgery but also provides precise 3D surgical planning. The visualization of treatment outcomes is forging a new paradigm for doctor-patient communication. Subject. The subject of this research is artificial intelligence algorithms for the automation of diagnosis, orthodontic treatment planning, and the prediction of its results. Aim. To conduct an analysis of the literature dedicated to the application of artificial intelligence technologies in orthodontic practice, in order to identify the key vectors of the digital transformation in this field of dentistry. Materials and Methods. A literature review from 2019 to 2025 on the application of artificial intelligence in orthodontics was conducted based on an analysis of the eLIBRARY, Scopus, Google Scholar, PubMed/MEDLINE, and MDPI databases. As a result of the analysis, 110 scientific studies were selected and examined in detail. Results. To date, the digital transformation of orthodontics has demonstrated significant progress through the implementation of neural network models. AI assists in identifying and classifying dentofacial anomalies from photographs, determining skeletal maturity stages, and performing comprehensive diagnosis, including identifying the causes of anomalies, cephalometric analysis, 3D modeling, assessment of temporomandibular joint (TMJ) condition, and facial scanning. These systems optimize clinicians' work: they speed up data analysis, provide a "second opinion" in complex cases, minimize diagnostic errors, and create clear visualizations of potential treatment outcomes. Conclusions 4. Modern technologies open up new possibilities for improving the quality of orthodontic care. 5. However, for the successful integration of AI, systemic challenges must be addressed: improving the quality of input data, preventing model overfitting, and conducting comprehensive clinical validation of the algorithms. 6. At the current stage, the field is transitioning from experimental developments to practical application, where the leading role remains with the clinician, and AI serves as a decision-support tool.

 искусственный интеллект машинное обучение нейросети ортодонтия ортодонтическое лечение автоматизация artificial intelligence machine learning neural networks orthodontics orthodontic treatment automation

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 Ayupova I.O., Morina A.V. Cifrovye metody analiza v ortodontii. V: Avvakumova N.P. red. Sintez nauk kak osnova razvitiya medicinskih znaniy. Sbornik materialov I Mezhvuzovskoy nauchno-prakticheskoy konferencii s mezhdunarodnym uchastiem; Samara; 18 dekabrya 2020 goda. Samara: Samarskiy gosudarstvennyy medicinskiy universitet; 2020. S. 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 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 Dobratulin K.S., Ayupova I.O., Gaydel' A.V. Neyronnye seti v diagnostike i planirovanii ortodonticheskogo lecheniya. V: Avvakumova N.P. red. Sintez nauk kak osnova razvitiya medicinskih znaniy. Sbornik materialov I Mezhvuzovskoy nauchno-prakticheskoy konferencii s mezhdunarodnym uchastiem; Samara; 18 dekabrya 2020 goda. Samara: Samarskiy gosudarstvennyy medicinskiy universitet; 2020. S. 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 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 Kabardova K.H., Kilova M.A., Deshev A.V. Cifrovaya transformaciya v 3D-cefalometrii. V: Perspektiva-2024. Materialy Mezhdunarodnoy nauchnoy konferencii studentov, aspirantov i molodyh uchenyh; Nal'chik; 26–29 aprelya 2024 goda. T. 3. Nal'chik: Kabardino-Balkarskiy gosudarstvennyy universitet im. H.M. Berbekova; 2024. S. 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 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 Kolsanov A.V., Popov N.V., Ayupova I.O., Dobratulin K.S., Gaydel' A.V., Ivleva A.I. Razrabotka programmnogo kompleksa diagnostiki zubochelyustnyh anomaliy s primeneniem neyronnyh setey. V: Informacionnye tehnologii i nanotehnologii (ITNT-2021). Sbornik trudov po materialam VII Mezhdunarodnoy konferencii i molodezhnoy shkoly; Samara; 20–24 sentyabrya 2021 goda. Tom 3. Samara: Samarskiy nacional'nyy issledovatel'skiy universitet imeni akademika S.P. Koroleva; 2021. Stat'ya 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 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 Oborotistov N.Yu., Muraev A.A., Sen'ko D.A. Innovacionnaya onlayn-platforma ViSurgery dlya rascheta telerentgenogramm s ispol'zovaniem iskusstvennogo intellekta. Ortodontiya. 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 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 Oborotistov N.Yu., Muraev A.A. Primenenie iskusstvennogo intellekta v stomatologii. Rossiyskaya stomatologiya. 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 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 Rahmataliev D.I., Podudanskiy P.A., Zryumov P.A. Razrabotka programmnogo obespecheniya dlya opredeleniya cefalometricheskih tochek na telerentgenogrammah. V: Sovremennye cifrovye tehnologii. Materialy III Vserossiyskoy nauchno-prakticheskoy konferencii; Barnaul; 03 iyunya 2024 goda. Barnaul: Altayskiy gosudarstvennyy tehnicheskiy universitet im. I.I. Polzunova; 2024. S. 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 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 Rahimberdiev R.A., Sayfulaeva A.A. Ispol'zovanie iskusstvennogo intellekta dlya prognoza rezul'tatov lecheniya v ortodontii. 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 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 Ruziev Sh., Nigmatov R. Bolalarda prognatik prikusni ortodontik davolashni takomillashtirish va uning samaradorligini sun'iy intellekt erdamida baҳolash (Uslubiy tavsiyanoma). 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 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 Rybakov Aleksandr. Optimizaciya ortodonticheskogo lecheniya na osnove neyronnyh setey, analiza konechnymi elementami i cifrovyh kart slizistoy polosti rta: dissertaciya na soiskanie uchenoy stepeni kandidata medicinskih nauk. Sankt-Peterburg; 2024. 235 s. [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 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 Saleev R.A., Zaynutdinova A.R., Mustakimova R.F. Ispol'zovanie iskusstvennogo intelekta Diagnocat v sovremennoy stomatologii. V: Aktual'nye voprosy stomatologii. Sbornik nauchnyh trudov, posvyaschennyy osnovatelyu kafedry ortopedicheskoy stomatologii KGMU, professoru Isaak Mihaylovichu Oksmanu. Kazan': Kazanskiy gosudarstvennyy medicinskiy universitet; 2025. S. 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 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 Simakova A.A., Gorbatova M.A., Rusanov D.S., Karyakin A.A., Grzhibovskiy A.M. Primenenie iskusstvennyh neyronnyh setey v praktike vracha-ortodonta: obzor rossiyskih issledovaniy za period 2013-2023. Vestnik novyh medicinskih tehnologiy. Elektronnoe izdanie. 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 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 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 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 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 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 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 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 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 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 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 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 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 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 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 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 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 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 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 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 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 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 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 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 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 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 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 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 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 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 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 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 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 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 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 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 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 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 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 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 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 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 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 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 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 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 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 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 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 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 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 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 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 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 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 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 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 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 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 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 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 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 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 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 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 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 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 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 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 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 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 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 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 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 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 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 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 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 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 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 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 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 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 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 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 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 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 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 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 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 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 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 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 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 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 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 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 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 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 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 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 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 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 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 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 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 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 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 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 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 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 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 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 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 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 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 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 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 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 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 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 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 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 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 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 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. 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. 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 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 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 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 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 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 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 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 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 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 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 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 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 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 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 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 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 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 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 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 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 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 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 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 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 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 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 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 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 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 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 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 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 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 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 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 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 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 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 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 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 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 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 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 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 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 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 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 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 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 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 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 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 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 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 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 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 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 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 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 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 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 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 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 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 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 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 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 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 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