机器学习在牙体缺损修复中的应用

doi:10.13591/j.cnki.kqyx.2024.07.012

Abstract

Abstract:

As an important branch of artificial intelligence, machine learning meets the demand for digital intelligence in prosthodontics with the rapid development and application of its algorithm. This review provides an overview of the application of machine learning algorithms in dental restoration, including the design of dental prostheses, tooth shade selection, and tooth preparation line detection. Additionally, this paper also briefly analyzes the merits of machine learning and discusses the challenges and issues in current research, to provide references for research on machine learning algorithms in prosthodontics.

Key words: machine learning, artificial intelligence, prosthodontics, dental defect

CLC Number:

R783.3

WANG Yue, LI Hangyun, TANG Wanyi, WU Junhua. Application of machine learning in restoration of dental defect[J]. Stomatology, 2024, 44(7): 551-550.

Figures/Tables 1

References 47

[1]	Lawrence DR, Palacios-González C, Harris J. Artificial intelligence[J]. Camb Q Healthc Ethics, 2016, 25(2):250-261. doi: 10.1017/S0963180115000559 pmid: 26957450
[2]	Steels L, Lopezde Mantaras R. The Barcelona declaration for the proper development and usage of artificial intelligence in Europe[J]. AI Commun, 2018, 31(6):485-494.
[3]	Montani S, Striani M. Artificial intelligence in clinical decision support: A focused literature survey[J]. Yearb MedInform, 2019, 28(1):120-127.
[4]	Chau RCW, Thu KM, Hsung RTC, et al. Teeth reconstruction using artificial intelligence: Trends, perspectives, and prospects[J]. J Calif Dent Assoc, 2023, 51(1):2199910.
[5]	Park WJ, Park JB. History and application of artificial neural networks in dentistry[J]. Eur J Dent, 2018, 12(4):594-601. doi: 10.4103/ejd.ejd_325_18 pmid: 30369809
[6]	Shehab M, Abualigah L, Shambour Q, et al. Machine learning in medical applications: A review of state-of-the-art methods[J]. Comput Biol Med, 2022, 145: 105458.
[7]	Mirza M, Osindero S. Conditional generative adversarial nets[DB/L]. DOI:10.48550/arXiv.1411.1784.
[8]	尹来国, 孙仁诚, 邵峰晶, 等. 多模式生成对抗网络[J]. 计算机应用研究, 2022, 39(6):1689-1693.
[9]	Shan T, TayFR, Gu L. Application of artificial intelligence in dentistry[J]. J Dent Res, 2021, 100(3):232-244. doi: 10.1177/0022034520969115 pmid: 33118431
[10]	Aminoshariae A, Kulild J, Nagendrababu V. Artificialintelligence in endodontics: Current applications and future directions[J]. J Endod, 2021, 47(9):1352-1357.
[11]	Fatima A, Shafi I, Afzal H, et al. Advancements in dentistry with artificial intelligence: Current clinical applications and future perspectives[J]. Healthcare, 2022, 10(11):2188.
[12]	Revilla-León M, Gómez-Polo M, Vyas S, et al. Artificial intelligence applications in implant dentistry: A systematic review[J]. J Prosthet Dent, 2023, 129(2):293-300.
[13]	Xiao DQ, Lian CF, Deng H, et al. Estimating reference bony shape models for orthognathic surgical planning using 3D point-cloud deep learning[J]. IEEE J Biomed Health Inform, 2021, 25(8):2958-2966.
[14]	俞懿强, 刘伟才. 口腔固定修复全数字化流程的临床应用现状及问题[J]. 中华口腔医学杂志, 2023, 58(5):398-403.
[15]	Blatz MB, Conejo J. The Current state of chairside digital dentistry and materials[J]. Dent Clin North Am, 2019, 63(2):175-197. doi: S0011-8532(18)30091-0 pmid: 30825985
[16]	Mehl A, Blanz V. New procedure for fully automatic occlusal surface reconstruction by means of a biogeneric tooth model[J]. Int J Comput Dent, 2005, 8(1):13-25. pmid: 15892521
[17]	Bohner LOL, Neto PT, Ahmed AS, et al. CEREC chairside system to register and design the occlusion in restorative dentistry: A systematic literature review[J]. J Esthet Restor Dent, 2016, 28(4):208-220. doi: 10.1111/jerd.12226 pmid: 27312653
[18]	Ferraris F. Posterior indirect adhesive restorations (PIAR):Preparation designs and adhesthetics clinical protocol[J]. Int J Esthet Dent, 2017, 12(4):482-502.
[19]	Tian SK, Wang MH, Yuan FL, et al. Efficient computer-aided design of dental inlay restoration: A deep adversarial framework[J]. IEEE Trans Med Imaging, 2021, 40(9):2415-2427.
[20]	Farook TH, Ahmed S, Jamayet NB, et al. Computer-aided design and 3-dimensional artificial/convolutional neural network for digital partial dental crown synthesis and validation[J]. Sci Rep, 2023, 13(1):1561. doi: 10.1038/s41598-023-28442-1 pmid: 36709380
[21]	Hwang JJ, Azernikov S, Efros AA, et al. Learning beyond human expertise with generative models for dental restorations[M]. arXiv:1804.00064( 2018).
[22]	袁福来, 戴宁, 田素坤, 等. 采用条件生成式对抗网络的缺损牙全冠修复技术[J]. 计算机辅助设计与图形学学报, 2019, 31(12):2113-2120.
[23]	Yuan FL, Dai N, Tian SK, et al. Personalized design technique for the dental occlusal surface based on conditional generative adversarial networks[J]. Int J Numer Method Biomed Eng, 2020, 36(5):e3321.
[24]	Tian SK, Huang RK, Li ZY, et al. A dual discriminator adversarial learning approach for dental occlusal surface reconstruction[J]. J Healthc Eng, 2022, 2022: 1933617.
[25]	Tian SK, Wang MH, Dai N, et al. DCPR-GAN: Dental crown prosthesis restoration using two-stage generative adversarial networks[J]. IEEE J Biomed Health Inform, 2022, 26(1):151-160.
[26]	Isola P, Zhu JY, Zhou TH, et al. Image-to-image translation with conditional adversarial networks[C]// 2017 IEEE ConferenceonComputer Vision and Pattern Recognition (CVPR). July 21-26, 2017, Honolulu, HI, USA. IEEE, 2017: 5967-5976.
[27]	Radford A, Metz L, Chintala S. Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks[M]. arXiv, 2016.
[28]	Wu JJ, Zhang CK, Xue TF, et al. Learning a probabilistic latent space of object shapes via 3D generative-adversarial modeling[C]// Proceedings of the 30th International Conference on Neural Information Processing Systems. December 5-10, 2016, Barcelona, Spain. New York: ACM, 2016: 82-90.
[29]	Chau RCW, Hsung RTC, McGrath C, et al. Accuracy of artificial intelligence-designed single-molar dental prostheses: A feasibility study[J]. J Prosthet Dent, 2023: S0022 -S3913(22)00764-8.
[30]	Lee YC, Lee C, Shim JS, et al. Comparison between occlusal errors of single posterior crowns adjusted using patient specific motion or conventional methods[J]. Appl Sci, 2020, 10(24):9140.
[31]	Ding H, Cui ZM, Maghami E, et al. Morphology and mechanical performance of dental crown designed by 3D-DCGAN[J]. Dent Mater, 2023, 39(3):320-332.
[32]	中华口腔医学会口腔修复学专业委员会. 椅旁计算机辅助设计与辅助制作全瓷修复技术指南[J]. 中华口腔医学杂志, 2022, 57(10):992-996.
[33]	Feng Y, Tao BX, Fan JC, et al. 3D reconstruction for maxillary anterior tooth crown based on shape and pose estimation networks[J]. Int J Comput Assist Radiol Surg, 2023, 18(8):1405-1416. doi: 10.1007/s11548-023-02841-1 pmid: 36754949
[34]	Sadowsky SJ. An overview of treatment considerations for esthetic restorations: A review of the literature[J]. J Prosthet Dent, 2006, 96(6):433-442. doi: 10.1016/j.prosdent.2006.09.018 pmid: 17174661
[35]	Tabatabaian F, Beyabanaki E, Alirezaei P, et al. Visual and digital tooth shade selection methods, related effective factors and conditions, and their accuracy and precision: A literature review[J]. J Esthet Restor Dent, 2021, 33(8):1084-1104. doi: 10.1111/jerd.12816 pmid: 34498789
[36]	Dagg H, O'Connell B, Claffey N, et al. The influence of some different factors on the accuracy of shade selection[J]. J Oral Rehabil, 2004, 31(9):900-904. doi: 10.1111/j.1365-2842.2004.01310.x pmid: 15369473
[37]	Wei JQ, Peng MD, Li Q, et al. Evaluation of a novel computer color matching system based on the improved back-propagation neural network model[J]. J Prosthodont, 2018, 27(8):775-783. doi: 10.1111/jopr.12561 pmid: 27860023
[38]	Kim M, Kim B, Park B, et al. A digital shade-matching device for dental color determination using the support vector machine algorithm[J]. Sensors, 2018, 18(9):3051.
[39]	Chen SL, Zhou HS, Chen TY, et al. Dental shade matching method based on hue, saturation, value color model with machine learning and fuzzy decision[J]. Sens Mater, 2020, 32(10):3185.
[40]	Wanna Y, Wiratchawa K, Leenaracharoongruang R, et al. DentShadeAI: a framework for automatic dental shade matching through mobile phone camera[C]// 2022 37th International Technical Conference on Circuits/Systems, Computers and Communications (ITC-CSCC). Phuket, Thailand. IEEE, 2022: 282-285.
[41]	Jahangiri L, Wahlers C, Hittelman E, et al. Assessment of sensitivity and specificity of clinical evaluation of cast restoration marginal accuracy compared to stereomicroscopy[J]. J Prosthet Dent, 2005, 93(2):138-142. doi: 10.1016/j.prosdent.2004.11.007 pmid: 15674223
[42]	Papadiochou S, Pissiotis AL. Marginal adaptation and CAD-CAM technology: A systematic review of restorative material and fabrication techniques[J]. J Prosthet Dent, 2018, 119(4):545-551. doi: S0022-3913(17)30488-2 pmid: 28967399
[43]	Joda T, Zarone F, Ferrari M. The complete digital workflow in fixed prosthodontics:A systematic review[J]. BMC Oral Health, 2017, 17(1):124. doi: 10.1186/s12903-017-0415-0 pmid: 28927393
[44]	Mai HN, Han JS, Kim HS, et al. Reliability of automatic finish line detection for tooth preparation in dental computer-aided software[J]. J Prosthodont Res, 2023, 67(1):138-143.
[45]	Zhang B, Dai N, Tian SK, et al. The extraction method of tooth preparation margin line based on S-Octree CNN[J]. Int J Numer Method Biomed Eng, 2019, 35(10):e3241.
[46]	张贝, 戴宁, 田素坤, 等. 结合稀疏八叉树卷积神经网络的牙齿预备体颈缘线提取方法[J]. 计算机辅助设计与图形学学报, 2019, 31(12):2129-2135.
[47]	Takahashi T, Nozaki K, Gonda T, et al. A system for designing removable partial dentures using artificial intelligence. Part 1. Classification of partially edentulous Arches using a convolutional neural network[J]. J Prosthodont Res, 2021, 65(1):115-118.

Application of machine learning in restoration of dental defect

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[6]	WU Yue, YAN Bin. Application of machine learning in orthodontics [J]. Stomatology, 2022, 42(1): 29-35.
[7]	. Application of chairside CAD/CAM technology in prosthodontics [J]. , 2019, 39(3): 275-279.
[8]	. Factors affecting the precision of 3D printing prosthesis [J]. , 2018, 38(9): 860-864.
[9]	. Application of polyetheretherketone（PEEK）in prosthodontics [J]. , 2017, 37(10): 957-960.
[10]	. The clinic research on removable prosthodontical treatment of children′s dentition defect [J]. , 2014, 34(4): 253-253.