medvisМедицинская визуализацияMedical Visualization1607-07632408-9516RDS-Media Ltd.10.24835/1607-0763-1103medvis-1103Research ArticleГОЛОВАHEADОбзор рентгенодиагностических on-line сервисов, основанных на искусственных нейронных сетях в стоматологииReview of online X-ray diagnostic services based on artificial neural networks in dentistryhttps://orcid.org/0000-0002-9421-600XМокренкоМ. Е.MokrenkoM. E.

Мокренко Марк Евгеньевич – аспирант кафедры челюстно-лицевой хирургии и хирургической стоматологии

117198 Москва, ул. Миклухо-Маклая, 6

Тел.: +7-950-101-03-00

Mark E. Mokrenko – graduate student of The Department of Oral and Maxillofacial Surgery

6, Miklukho-Maklay str., Moscow 117198

markmokrenko@yandex.ruhttps://orcid.org/0000-0001-7160-2023ГусейновН. А.GuseynovN. A.

Гусейнов Ниджат Айдын оглы – аспирант кафедры челюстно-лицевой хирургии и хирургической стоматологии

117198 Москва, ул. Миклухо-Маклая, 6

Guseynov Nidjat A.O. – graduate student of The Department of Oral and Maxillofacial Surgery

6, Miklukho-Maklay str., Moscow 117198

Nid.gus@mail.ruhttps://orcid.org/0000-0001-9429-158XАль ХаффарЖ.AlhaffarJ.

Аль Хаффар Жаклин – аспирант кафедры челюстно-лицевой хирургии и хирургической стоматологии

117198 Москва, ул. Миклухо-Маклая, 6

Jacqueline Alhaffar – graduate student of The Department of Oral and Maxillofacial Surgery

6, Miklukho-Maklay str., Moscow 117198

jacqueline.alhaffar@gmail.comhttps://orcid.org/0000-0001-8048-5703ТутуровН. С.TuturovN. S.

Тутуров Николай Станиславович – канд. мед. наук, доцент кафедры стоматологии детского возраста и ортодонтии

117198 Москва, ул. Миклухо-Маклая, 6

Nikolay S. Tuturov – Cand. of Sci (Med.), Associate Professor of the Department of Pediatric Dentistry and Orthodontics

6, Miklukho-Maklay str., Moscow 117198

ntuturov@mail.ruhttps://orcid.org/0000-0002-4132-9377СаркисянМ. С.SarkisyanM. S.

Саркисян Мартирос Сергеевич – доктор мед. наук, доцент кафедры ортопедической стоматологии

117198 Москва, ул. Миклухо-Маклая, 6

Martiros S. Sarkisyan – Doct. of Sci (Med.),  assistant professor of the Department of Prosthetic Dentistry

6, Miklukho-Maklay str., Moscow 117198

sarkisyan_ms@pfur.ruФГАОУ ВО “Российский университет дружбы народов” Минобрнауки РоссииРоссияThe Peoples' Friendship University of RussiaRussian Federation202211082022263114122Copyright © Мокренко М.Е., Гусейнов Н.А., Аль Хаффар Ж., Тутуров Н.С., Саркисян М.С., 20222022Мокренко М.Е., Гусейнов Н.А., Аль Хаффар Ж., Тутуров Н.С., Саркисян М.С.Mokrenko M.E., Guseynov N.A., Alhaffar J., Tuturov N.S., Sarkisyan M.S.Данная работа распространяется под лицензией Creative Commons Attribution 4.0.This work is licensed under a Creative Commons Attribution 4.0 License.https://medvis.rdsmedia.ru/jour/article/view/1103Цель исследования

Цель исследования: анализ доступных on-line сервисов и программ, использующих искусственные нейронные сети (ИНС), в стоматологии, в особенности для цефалометрического анализа.

Материал и методы

Материал и методы. Проведен поиск научных публикаций в информационно-аналитических системах PubMed, Google Scholar и eLibrary без ограничения по срокам публикации по комбинациям из следующих ключевых слов: artificial intelligence, deep learning, computer vision, neural network, dentistry, orthodontics, cephalometry, cephalometric analysis. Были проанализированы 1612 статей, из которых 23 публикации использованы для составления обзора.

Результаты

Результаты. Глубокое машинное обучение на основе ИНС успешно применяется в различных разделах медицины в качестве аналитического инструмента для обработки различных данных. Особенно успешно ИНС применяются для распознавания изображений в рентгенологии и гистологии. В частности, в стоматологии компьютерное зрение используется для диагностики заболеваний челюстно-лицевой области, планирования оперативного лечения, в том числе имплантации, а также для цефалометрического анализа для нужд врачей-ортодонтов и челюстно-лицевых хирургов.

Заключение

Заключение. В настоящее время существует множество программ и on-line сервисов для цефалометрического анализа. Однако лишь 7 из них используют ИНС для автоматической разметки и анализа снимков. Также недостаточно данных для оценки точности их работы и удобства.

Aim

Aim. This review is devoted to the analysis of available on-line services and programs using artificial neural networks (ANNs) in dentistry, especially for cephalometric analysis.

Materials and methods

Materials and methods. We searched for scientific publications in the information and analytical databases PubMed, Google Scholar and eLibrary using combinations of the following keywords: artificial intelligence, deep learning, computer vision, neural network, dentistry, orthodontics, cephalometry, cephalometric analysis. 1612 articles were analyzed, of which 23 publications were included in our review.

Results

Results. Deep machine learning based on ANN has been successfully used in various branches of medicine as an analytical tool for processing various data. ANNs are especially successfully used for image recognition in radiology and histology. In dentistry, computer vision is used to diagnose diseases of the maxillofacial region, plan surgical treatment, including dental implantation, as well as for cephalometric analysis for the needs of orthodontists and maxillofacial surgeons.

Conclusion

Conclusion. Currently, there are many programs and on-line services for cephalometric analysis. However, only 7 of them use ANNs for automatic landmarking and image analysis. Also, there is not enough data to evaluate the accuracy of their work and convenience.

Искусственная нейронная сетьискусственный интеллектрентгенологияцефалометрический анализортодонтиячелюстно-лицевая хирургияartificial neural networkartificial intelligenceradiologycephalometric analysisorthodonticsmaxillofacial surgeryReferencesSteimann F. On the use and usefulness of fuzzy sets in medical AI. Artif. Intell. Med. 2001; 21 (1–3): 131–137. http://doi.org/10.1016/s0933-3657(00)00077-4Steimann F. On the use and usefulness of fuzzy sets in medical AI. Artif. Intell. Med. 2001; 21 (1–3): 131–137. http://doi.org/10.1016/s0933-3657(00)00077-4McCulloch W.S., Pitts W. A logical calculus of the ideas imminent in nervous activity. Bull. Math. Biophys. 1943; 5: 115–133.McCulloch W.S., Pitts W. A logical calculus of the ideas imminent in nervous activity. Bull. Math. Biophys. 1943; 5: 115–133.Poulton M.M. Computational neural networks for geophysical data processing: Elsevier, 2001. 10 p.Poulton M.M. Computational neural networks for geophysical data processing: Elsevier, 2001. 10 p.Newell A., Simon H.A. Computer science as empirical inquiry: Symbols and search. Commun. ACM. 1976; 19: 113 126.Newell A., Simon H.A. Computer science as empirical inquiry: Symbols and search. Commun. ACM. 1976; 19: 113 126.Nilsson N. The Quest for Artificial Intelligence. New York: Cambridge University Press, 2010.Nilsson N. The Quest for Artificial Intelligence. New York: Cambridge University Press, 2010.Rosenblatt F. The Perceptron: a probabilistic model for information storage and organization in the brain. Psychol. Rev. 1958; 65: 386–408.Rosenblatt F. The Perceptron: a probabilistic model for information storage and organization in the brain. Psychol. Rev. 1958; 65: 386–408.Bowling M., Fürnkranz J., Graepel T., Musick R. Machine learning and games. Mach. Learn. 2006; 63: 211 215. http://doi.org/10.1007/s10994-006-8919-xBowling M., Fürnkranz J., Graepel T., Musick R. Machine learning and games. Mach. Learn. 2006; 63: 211 215. http://doi.org/10.1007/s10994-006-8919-xWerbos P. Beyond regression: new tools for prediction and analysis in the behavioral sciences. PhD Thesis, Harvard University, 1974.Werbos P. Beyond regression: new tools for prediction and analysis in the behavioral sciences. PhD Thesis, Harvard University, 1974.Hopfield J.J. Neural networks and physical systems with emergent collective computational abilities. Proc. Natl. Acad. Sci. USA. 1982; 79: 2554–2558.Hopfield J.J. Neural networks and physical systems with emergent collective computational abilities. Proc. Natl. Acad. Sci. USA. 1982; 79: 2554–2558.Park J., Sandberg I.W. Universal approximation using radial-basis-function networks. Neural. Comput. 1991; 3: 246–257.Park J., Sandberg I.W. Universal approximation using radial-basis-function networks. Neural. Comput. 1991; 3: 246–257.Carpenter G.A., Grossberg S. The ART of adaptive pattern recognition by a self-organizing neural network. Computer. 1988; 21: 77–88.Carpenter G.A., Grossberg S. The ART of adaptive pattern recognition by a self-organizing neural network. Computer. 1988; 21: 77–88.Baxt W.G. Use of an artificial neural network for data analysis in clinical decision-making: the diagnosis of acute coronary occlusion. Neural. Comput. 1990; 2: 480–489.Baxt W.G. Use of an artificial neural network for data analysis in clinical decision-making: the diagnosis of acute coronary occlusion. Neural. Comput. 1990; 2: 480–489.Boon M.E., Kok L.P. Neural network processing can provide means to catch errors that slip through human screening of pap smears. Diagn. Cytopathol. 1993; 9: 411–416.Boon M.E., Kok L.P. Neural network processing can provide means to catch errors that slip through human screening of pap smears. Diagn. Cytopathol. 1993; 9: 411–416.Muraev A.A., Tsai P., Kibardin I. et al. Frontal cephalometric landmarking: humans vs artificial neural networks. Int. J. Comput. Dent. 2020; 23 (2): 139–148.Muraev A.A., Tsai P., Kibardin I. et al. Frontal cephalometric landmarking: humans vs artificial neural networks. Int. J. Comput. Dent. 2020; 23 (2): 139–148.Muraev A.A., Kibardin I.A., Oborotistov N.Yu., Ivanov S.S. Use of neural network algorithms for the automated arrangement of cephalometric markers on lateral cefalograms. REJR. 2018; 8 (4): 16–22. http://doi.org/10.21569/2222-7415-2018-8-2-16-22Muraev A.A., Kibardin I.A., Oborotistov N.Yu., Ivanov S.S. Use of neural network algorithms for the automated arrangement of cephalometric markers on lateral cefalograms. REJR. 2018; 8 (4): 16–22. http://doi.org/10.21569/2222-7415-2018-8-2-16-22Ezhov M., Gusarev M., Golitsyna M., et al. Clinically applicable artificial intelligence system for dental diagnosis with CBCT. Sci. Rep. 2021; 11 (1): 15006. http://doi.org/10.1038/s41598-021-94093-9Ezhov M., Gusarev M., Golitsyna M., et al. Clinically applicable artificial intelligence system for dental diagnosis with CBCT. Sci. Rep. 2021; 11 (1): 15006. http://doi.org/10.1038/s41598-021-94093-9Solovyh E.A., Obrubov A.A., Arranz I. et al. Artificial Intelligence DENTOMO: Opportunities and Prospects for Interpretation of Cone Beam CT in Dentistry. Bull. Exp. Biol. Med. 2021; 170 (5): 686–688. http://doi.org/10.1007/s10517-021-05133-3Solovyh E.A., Obrubov A.A., Arranz I. et al. Artificial Intelligence DENTOMO: Opportunities and Prospects for Interpretation of Cone Beam CT in Dentistry. Bull. Exp. Biol. Med. 2021; 170 (5): 686–688. http://doi.org/10.1007/s10517-021-05133-3Lee J.H., Kim D.H., Jeong S.N., Choi S.H. Detection and diagnosis of dental caries using a deep learning-based convolutional neural network algorithm. J. Dent. 2018; 77: 106–111. http://doi.org/10.1016/j.jdent.2018.07.015Lee J.H., Kim D.H., Jeong S.N., Choi S.H. Detection and diagnosis of dental caries using a deep learning-based convolutional neural network algorithm. J. Dent. 2018; 77: 106–111. http://doi.org/10.1016/j.jdent.2018.07.015Kim E.Y., Lim K.O., Rhee H.S. Predictive modeling of dental pain using neural network. Stud. Health Technol. Inform. 2009; 146: 745 746.Kim E.Y., Lim K.O., Rhee H.S. Predictive modeling of dental pain using neural network. Stud. Health Technol. Inform. 2009; 146: 745 746.Prados-Privado M., García Villalón J., Martínez-Martínez C.H. et al. Dental Caries Diagnosis and Detection Using Neural Networks: A Systematic Review. J. Clin. Med. 2020; 9 (11): 3579. http://doi.org/10.3390/jcm9113579Prados-Privado M., García Villalón J., Martínez-Martínez C.H. et al. Dental Caries Diagnosis and Detection Using Neural Networks: A Systematic Review. J. Clin. Med. 2020; 9 (11): 3579. http://doi.org/10.3390/jcm9113579Schwendicke F., Golla T., Dreher M., Krois J. Convolutional neural networks for dental image diagnostics: A scoping review. J. Dent. 2019; 91: 103226. http://doi.org/10.1016/j.jdent.2019.103226Schwendicke F., Golla T., Dreher M., Krois J. Convolutional neural networks for dental image diagnostics: A scoping review. J. Dent. 2019; 91: 103226. http://doi.org/10.1016/j.jdent.2019.103226Orhan K., Bayrakdar I.S., Ezhov M., Kravtsov A., Özyürek T. Evaluation of artificial intelligence for detecting periapical pathosis on cone-beam computed tomography scans. Int. Endod. J. 2020; 53 (5): 680–689. http://doi.org/10.1111/iej.13265Orhan K., Bayrakdar I.S., Ezhov M., Kravtsov A., Özyürek T. Evaluation of artificial intelligence for detecting periapical pathosis on cone-beam computed tomography scans. Int. Endod. J. 2020; 53 (5): 680–689. http://doi.org/10.1111/iej.13265Orhan K., Bilgir E., Bayrakdar I.S. et al. Evaluation of artificial intelligence for detecting impacted third molars on cone-beam computed tomography scans. J. Stomatol. Oral. Maxillofac. Surg. 2021; 122 (4): 333–337. http://doi.org/10.1016/j.jormas.2020.12.006Orhan K., Bilgir E., Bayrakdar I.S. et al. Evaluation of artificial intelligence for detecting impacted third molars on cone-beam computed tomography scans. J. Stomatol. Oral. Maxillofac. Surg. 2021; 122 (4): 333–337. http://doi.org/10.1016/j.jormas.2020.12.006Bayrakdar K.S., Orhan K., Bayrakdar I.S. et al. A deep learning approach for dental implant planning in conebeam computed tomography images. BMC Med. Imaging. 2021; 21 (1): 86. http://doi.org/10.1186/s12880-021-00618-zBayrakdar K.S., Orhan K., Bayrakdar I.S. et al. A deep learning approach for dental implant planning in conebeam computed tomography images. BMC Med. Imaging. 2021; 21 (1): 86. http://doi.org/10.1186/s12880-021-00618-zSiddiqui N.R., Hodges S., Sharif M.O. Availability of orthodontic smartphone apps. J. Orthod. 2019; 46 (3): 235–241. http://doi.org/10.1177/1465312519851183Siddiqui N.R., Hodges S., Sharif M.O. Availability of orthodontic smartphone apps. J. Orthod. 2019; 46 (3): 235–241. http://doi.org/10.1177/1465312519851183Мураев А.А., Гусейнов Н.А., Цай П.А., Кибардин И.А., Буренчев Д.В., Иванов С.С., Оборотистов Н.Ю., Матюта М.А., Грачев Н.С., Ларин С.С. Искусственные нейронные сети в лучевой диагностике, в стоматологии и в челюстно-лицевой хирургии (обзор литературы). Клиническая стоматология. 2020; 3 (95): 72–80. http://doi.org/10.37988/1811-153X_2020_3_76Muraev A.A., Guseynov N.A., Tsay P.A., Kibardin I.A., Burenchev D.V., Ivanov S.S., Oborotistov N.Yu., Matuta M.A., Grachev N.S., Larin S.S. Artificial neural networks in dental and maxillofacial radiology: a review. Clinical Dentistry. 2020; 3 (95): 72–80. (In Russian)Broadbent B. A new X-ray technique and its application to orthodontia. Angle Orthod. 1931; 1: 45–66.Broadbent B. A new X-ray technique and its application to orthodontia. Angle Orthod. 1931; 1: 45–66.Wang C.W., Huang C.T., Hsieh M.C. et al. Evaluation and Comparison of Anatomical Landmark Detection Methods for Cephalometric X-Ray Images: A Grand Challenge. IEEE Trans. Med. Imaging. 2015; 34 (9): 1890–1900. http://doi.org/10.1109/TMI.2015.2412951Wang C.W., Huang C.T., Hsieh M.C. et al. Evaluation and Comparison of Anatomical Landmark Detection Methods for Cephalometric X-Ray Images: A Grand Challenge. IEEE Trans. Med. Imaging. 2015; 34 (9): 1890–1900. http://doi.org/10.1109/TMI.2015.2412951Wang C.W., Huang C.T., Lee J.H. et al. A benchmark for comparison of dental radiography analysis algorithms. Med. Image Anal. 2016; 31: 63–76. http://doi.org/10.1016/j.media.2016.02.004Wang C.W., Huang C.T., Lee J.H. et al. A benchmark for comparison of dental radiography analysis algorithms. Med. Image Anal. 2016; 31: 63–76. http://doi.org/10.1016/j.media.2016.02.004Alam M.K., Alfawzan A.A. Dental Characteristics of Different Types of Cleft and Non-cleft Individuals. Front. Cell. Dev. Biol. 2020; 8: 789. http://doi.org/10.3389/fcell.2020.00789Alam M.K., Alfawzan A.A. Dental Characteristics of Different Types of Cleft and Non-cleft Individuals. Front. Cell. Dev. Biol. 2020; 8: 789. http://doi.org/10.3389/fcell.2020.00789Yassir Y.A., Salman A.R., Nabbat S.A. The accuracy and reliability of WebCeph for cephalometric analysis. J. Taibah. Univ. Med. Sci. 2021; 17 (1): 57–66. http://doi.org/10.1016/j.jtumed.2021.08.010Yassir Y.A., Salman A.R., Nabbat S.A. The accuracy and reliability of WebCeph for cephalometric analysis. J. Taibah. Univ. Med. Sci. 2021; 17 (1): 57–66. http://doi.org/10.1016/j.jtumed.2021.08.010Alqahtani H. Evaluation of an online website-based platform for cephalometric analysis. J. Stomatol. Oral. Maxillofac. Surg. 2020; 121 (1): 53–57. http://doi.org/10.1016/j.jormas.2019.04.017Alqahtani H. Evaluation of an online website-based platform for cephalometric analysis. J. Stomatol. Oral. Maxillofac. Surg. 2020; 121 (1): 53–57. http://doi.org/10.1016/j.jormas.2019.04.017Meriç P., Naoumova J. Web-based Fully Automated Cephalometric Analysis: Comparisons between Appaided, Computerized, and Manual Tracings. Turk. J. Orthod. 2020; 33 (3): 142–149. Published 2020 Aug 11. http://doi.org/10.5152/TurkJOrthod.2020.20062Meriç P., Naoumova J. Web-based Fully Automated Cephalometric Analysis: Comparisons between Appaided, Computerized, and Manual Tracings. Turk. J. Orthod. 2020; 33 (3): 142–149. Published 2020 Aug 11. http://doi.org/10.5152/TurkJOrthod.2020.20062Silva T.P., Hughes M.M., Menezes L.D.S. et al.Artificial intelligence-based cephalometric landmark annotation and measurements according to Arnett's analysis: can we trust a bot to do that? Dentomaxillofac Radiol. 2021; 20200548. http://doi.org/10.1259/dmfr.20200548Silva T.P., Hughes M.M., Menezes L.D.S. et al.Artificial intelligence-based cephalometric landmark annotation and measurements according to Arnett's analysis: can we trust a bot to do that? Dentomaxillofac Radiol. 2021; 20200548. http://doi.org/10.1259/dmfr.20200548Mamta J., Poojita G., Ravinder K. et al. A review on cephalometric landmark detection techniques. Biomed. Signal Processing Control. 2021; 66: 102486. http://doi.org/10.1016/j.bspc.2021.102486Mamta J., Poojita G., Ravinder K. et al. A review on cephalometric landmark detection techniques. Biomed. Signal Processing Control. 2021; 66: 102486. http://doi.org/10.1016/j.bspc.2021.102486Rao G.K.L., Mokhtar N., Iskandar Y.H.P., Srinivasa A.C. Learning orthodontic cephalometry through augmented reality: A conceptual machine learning validation approach. 2018 International Conference on Electrical Engineering and Informatics (ICELTICs). 2018; 133–138. http://doi.org/10.1109/ICELTICS.2018.8548939Rao G.K.L., Mokhtar N., Iskandar Y.H.P., Srinivasa A.C. Learning orthodontic cephalometry through augmented reality: A conceptual machine learning validation approach. 2018 International Conference on Electrical Engineering and Informatics (ICELTICs). 2018; 133–138. http://doi.org/10.1109/ICELTICS.2018.8548939Sawchuk D., Alhadlaq A., Alkhadra T. et al. Comparison of two three-dimensional cephalometric analysis computer software. J. Orthod. Sci. 2014; 3 (4): 111–117. http://doi.org/10.4103/2278-0203.143230Sawchuk D., Alhadlaq A., Alkhadra T. et al. Comparison of two three-dimensional cephalometric analysis computer software. J. Orthod. Sci. 2014; 3 (4): 111–117. http://doi.org/10.4103/2278-0203.143230

The authors declare that there are no conflicts of interest present.