Медицинская литература. Новинки

 

 

 

 

 

 

Обзор рентгенодиагностических on-line сервисов, основанных на искусственных нейронных сетях в стоматологии

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

Ключевые слова:
искусственная нейронная сеть, искусственный интеллект, рентгенология, цефалометрический анализ, ортодонтия, челюстно-лицевая хирургия, artificial neural network, artificial intelligence, radiology, cephalometric analysis, orthodontics, maxillofacial surgery

Литература:
1.Steimann 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)00077-4
2.McCulloch W.S., Pitts W. A logical calculus of the ideas imminent in nervous activity. Bull. Math. Biophys. 1943; 5: 115-133.
3.Poulton M.M.Computational neural networks for geophysical data processing: Elsevier, 2001. 10 p.
4.Newell A., Simon H.A.Computer science as empirical inquiry: Symbols and search.Commun. ACM. 1976; 19: 113 126.
5.Nilsson N. The Quest for Artificial Intelligence. New York: Cambridge University Press, 2010.
6.Rosenblatt F. The Perceptron: a probabilistic model for information storage and organization in the brain. Psychol. Rev. 1958; 65: 386-408.
7.Bowling M., Furnkranz J., Graepel T, Musick R. Machine learning and games. Mach. Learn. 2006; 63: 211 215. http://doi.org/10.1007/s10994-006-8919-x
8.Werbos P. Beyond regression: new tools for prediction and analysis in the behavioral sciences. PhD Thesis, Harvard University, 1974.
9.Hopfield J.J. Neural networks and physical systems with emergent collective computational abilities. Proc. Natl. Acad. Sci. USA. 1982; 79: 2554-2558.
10.Park J., Sandberg I.W. Universal approximation using radial-basis-function networks. Neural.Comput. 1991; 3: 246-257.
11.Carpenter G.A., Grossberg S. The ART of adaptive pattern recognition by a self-organizing neural network.Computer. 1988; 21: 77-88.
12.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.
13.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.
14.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.
15.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 cefalo- grams. REJR. 2018; 8 (4): 16-22. http://doi.org/10.21569/2222-7415-2018-8-2-16-22
16.Ezhov 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/s41598-021-94093-9
17.Solovyh 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/s10517-021-05133-3
18.Lee 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/jJdent.2018.07.015
19.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.
20.Prados-Privado M., Garcfa Villalon J., Martfnez-Martfnez 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/jcm9113579
21.Schwendicke 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/jJdent.2019.103226
22.Orhan K., Bayrakdar I.S., Ezhov M., Kravtsov A., Ozyurek 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.13265
23.Orhan 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/jJormas.2020.12.006
24.Bayrakdar K.S., Orhan K., Bayrakdar I.S. et al. A deep learning approach for dental implant planning in cone-beam computed tomography images. BMC Med. Imaging. 2021; 21 (1): 86. http://doi.org/10.1186/s12880-021-00618-z
25.Siddiqui N.R., Hodges S., Sharif M.O. Availability of orthodontic smartphone apps. J. Orthod. 2019; 46 (3): 235-241. http://doi.org/10.1177/1465312519851183
26.Мураев 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_76 @@ Muraev 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)
27.Broadbent B. A new X-ray technique and its application to orthodontia. Angle Orthod. 1931; 1: 45-66.
28.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.2015.2412951
29.Wang 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.2016.02.004
30.Alam 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.2020.00789
31.Yassir 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.2021.08.010
32.Alqahtani 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.2019.04.017
33.Merig 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/TurkJ0rthod.2020.20062
34.Silva 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.20200548
35.Mamta 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.2021.102486
36.Rao 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.2018.8548939
37.Sawchuk 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

Review of online X-ray diagnostic services based on artificial neural networks in dentistry

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. 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. 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. 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.

Keywords:
искусственная нейронная сеть, искусственный интеллект, рентгенология, цефалометрический анализ, ортодонтия, челюстно-лицевая хирургия, artificial neural network, artificial intelligence, radiology, cephalometric analysis, orthodontics, maxillofacial surgery