人工智能预测正畸面部变化的研究进展和准确度：概况性系统综述

朱嘉珺; 杨昱新; 王海明

doi:10.1631/jzus.B2300244

Your Location：

Home >

Browse articles >

人工智能预测正畸面部变化的研究进展和准确度：概况性系统综述

Scan for full text

文章被引用时，请邮件提醒。

Submit

Research Articles | Updated：2023-11-15

人工智能预测正畸面部变化的研究进展和准确度：概况性系统综述
Development and accuracy of artificial intelligence-generated prediction of facial changes in orthodontic treatment: a scoping review
浙江大学学报（英文版）（B辑：生物医学和生物技术） 2023年24卷第11期页码：974-984
Affiliations：

1.Stomatology Hospital, School of Stomatology, Zhejiang University School of Medicine, Zhejiang Provincial Clinical Research Center for Oral Diseases, Key Laboratory of Oral Biomedical Research of Zhejiang Province, Cancer Center of Zhejiang University, Engineering Research Center of Oral Biomaterials and Devices of Zhejiang Province, Hangzhou 310000, China
2.Faculty of Dentistry, The University of Hong Kong, Hong Kong, China
Funds：

the Research Grants Council of the Hong Kong, China(17109619)
DOI：10.1631/jzus.B2300244
中图分类号：
CSTR：

朱嘉珺,杨昱新,王海明.人工智能预测正畸面部变化的研究进展和准确度：概况性系统综述[J].浙江大学学报（英文版）（B辑：生物医学和生物技术）,2023,24(11):974-984.

Jiajun ZHU, Yuxin YANG, Hai Ming WONG. Development and accuracy of artificial intelligence-generated prediction of facial changes in orthodontic treatment: a scoping review[J]. Journal of Zhejiang University-SCIENCE B (Biomedicine & Biotechnology), 2023,24(11):974-984.
朱嘉珺,杨昱新,王海明.人工智能预测正畸面部变化的研究进展和准确度：概况性系统综述[J].浙江大学学报（英文版）（B辑：生物医学和生物技术）,2023,24(11):974-984. DOI： 10.1631/jzus.B2300244.

Jiajun ZHU, Yuxin YANG, Hai Ming WONG. Development and accuracy of artificial intelligence-generated prediction of facial changes in orthodontic treatment: a scoping review[J]. Journal of Zhejiang University-SCIENCE B (Biomedicine & Biotechnology), 2023,24(11):974-984. DOI： 10.1631/jzus.B2300244.

摘要

近年来，人工智能（AI）被应用于分析和预测正畸面部软组织变化，然而其可靠性尚缺乏系统性评价。本综述概述了AI预测正畸面部变化的研究进展，并对其预测准确度进行综合分析。我们检索了包括PubMed、EBSCO,host,、Web of Science、Embase、Cochrane Library和Scopus在内的6个电子数据库（检索日期截至2023年3月14日），纳入了所有使用AI系统对正畸面部变化进行预测的临床研究，并应用QUADAS-2评价表和JBI对诊断性试验的评价表对纳入研究进行偏倚风险分析，同时应用GRADE评价系统进行证据分级。在筛选了2500项研究后，最终有4项非随机临床试验被纳入全文评价。低水平证据表明，AI预测正畸面部变化的总体准确度很高，但其对于下唇和颏部的预测准确度较低。此外，AI通过多模态融合模拟预测的面部形态被认为是合理真实的。然而，由于所有纳入的非随机对照试验研究都显示出中度至高度偏倚风险，因此还需要更多更严谨的临床研究来证实AI在正畸面部变化预测方面的应用价值。

Abstract

Artificial intelligence (AI) has been utilized in soft-tissue analysis and prediction in orthodontic treatment planning, although its reliability has not been systematically assessed. This scoping review was conducted to outline the development of AI in terms of predicting soft-tissue changes after orthodontic treatment, as well as to comprehensively evaluate its prediction accuracy. Six electronic databases (PubMed, EBSCO,host, Web of Science, Embase, Cochrane Library, and Scopus) were searched up to March 14, 2023. Clinical studies investigating the performance of AI-based systems in predicting post-orthodontic soft-tissue alterations were included. The Quality Assessment of Diagnostic Accuracy Studies-2 (QUADAS-2) and Joanna Briggs Institute (JBI) appraisal checklist for diagnostic test accuracy studies were applied to assess risk of bias, while the Grading of Recommendation, Assessment, Development, and Evaluation (GRADE) assessment was conducted to evaluate the certainty of outcomes. After screening 2500 studies, four non-randomized clinical trials were finally included for full-text evaluation. We found a low level of evidence indicating an estimated high overall accuracy of AI-generated prediction, whereas the lower lip and chin seemed to be the least predictable regions. Furthermore, the facial morphology simulated by AI via the fusion of multimodality images was considered to be reasonably true. Since all of the included studies that were not randomized clinical trials (non-RCTs) showed a moderate to high risk of bias, more well-designed clinical trials with sufficient sample size are needed in future work.

关键词

面部形态软组织变化人工智能（AI）正畸治疗

Keywords

Facial morphologySoft-tissue changesArtificial intelligence (AI)Orthodontic treatment

references

Abiodun OI, Jantan A, Omolara AE, et al., 2019. Comprehensive review of artificial neural network applications to pattern recognition. IEEE Access, 7:158820-158846. https://doi.org/10.1109/ACCESS.2019.2945545https://doi.org/10.1109/ACCESS.2019.2945545

Bral A, Olate S, Zaror C, et al., 2020. A prospective study of soft- and hard-tissue changes after mandibular advancement surgery: midline changes in the chin area. Am J Orthod Dentofacial Orthop, 157(5):662-667. https://doi.org/10.1016/j.ajodo.2019.05.022https://doi.org/10.1016/j.ajodo.2019.05.022

Campbell JM, Klugar M, Ding S, et al., 2020. Chapter 9: Diagnostic test accuracy systematic reviews. In: Aromataris E, Munn Z (Eds.), JBI Manual for Evidence Synthesis. JBI, p.309-359. https://doi.org/10.46658/JBIMES-20-10https://doi.org/10.46658/JBIMES-20-10

Chen S, Lou HD, Guo L, et al., 2012. 3-D finite element modelling of facial soft tissue and preliminary application in orthodontics. Comput Methods Biomech Biomed Engin, 15(3):255-261. https://doi.org/10.1080/10255842.2010.522188https://doi.org/10.1080/10255842.2010.522188

Graf CC, Dritsas K, Ghamri M, et al., 2022. Reliability of cephalometric superimposition for the assessment of craniofacial changes: a systematic review. Eur J Orthod, 44(5):477-490. https://doi.org/10.1093/ejo/cjab082https://doi.org/10.1093/ejo/cjab082

Holdaway RA, 1983. A soft-tissue cephalometric analysis and its use in orthodontic treatment planning. Part I. Am J Orthod, 84(1):1-28. https://doi.org/10.1016/0002-9416(83)90144-6https://doi.org/10.1016/0002-9416(83)90144-6

Howard J, 2019. Artificial intelligence: implications for the future of work. Am J Ind Med, 62(11):917-926. https://doi.org/10.1002/ajim.23037https://doi.org/10.1002/ajim.23037

Javid AM, Das S, Skoglund M, et al., 2021. A ReLU dense layer to improve the performance of neural networks. Proceedings of the IEEE International Conference on Acoustics, Speech and Signal Processing, p.2810-2814. https://doi.org/10.1109/ICASSP39728.2021.9414269https://doi.org/10.1109/ICASSP39728.2021.9414269

Karatas OH, Toy E, 2014. Three-dimensional imaging techniques: a literature review. Eur J Dent, 8(1):132-140. https://doi.org/10.4103/1305-7456.126269https://doi.org/10.4103/1305-7456.126269

Kasai K, 1998. Soft tissue adaptability to hard tissues in facial profiles. Am J Orthod Dentofacial Orthop, 113(6):‍674-684. https://doi.org/10.1016/s0889-5406(98)70228-8https://doi.org/10.1016/s0889-5406(98)70228-8

Kassem HE, Marzouk ES, 2018. Prediction of changes due to mandibular autorotation following miniplate-anchored intrusion of maxillary posterior teeth in open bite cases. Prog Orthod, 19:13. https://doi.org/10.1186/s40510-018-0213-5https://doi.org/10.1186/s40510-018-0213-5

Khanagar SB, Al-Ehaideb A, Vishwanathaiah S, et al., 2021. Scope and performance of artificial intelligence technology in orthodontic diagnosis, treatment planning, and clinical decision-making ‍– a systematic review. J Dent Sci, 16(1):482-492. https://doi.org/10.1016/j.jds.2020.05.022https://doi.org/10.1016/j.jds.2020.05.022

Leonardi R, Giordano D, Maiorana F, 2009. An evaluation of cellular neural networks for the automatic identification of cephalometric landmarks on digital images. J Biomed Biotechnol, 2009:717102. https://doi.org/10.1155/2009/717102https://doi.org/10.1155/2009/717102

Lim YN, Yang BE, Byun SH, et al., 2022. Three-dimensional digital image analysis of skeletal and soft tissue points A and B after orthodontic treatment with premolar extraction in bimaxillary protrusive patients. Biology (Basel), 11(3):381. https://doi.org/10.3390/biology11030381https://doi.org/10.3390/biology11030381

Liu CX, Kong DH, Wang SF, et al., 2021. Deep3D reconstruction: methods, data, and challenges. Front Inform Technol Electron Eng, 22(5):652-672. https://doi.org/10.1631/FITEE.2000068https://doi.org/10.1631/FITEE.2000068

Lux CJ, Stellzig A, Volz D, et al., 1998. A neural network approach to the analysis and classification of human craniofacial growth. Growth Dev Aging, 62(3):95-106.

Moon JH, Kim MG, Hwang HW, et al., 2022. Evaluation of an individualized facial growth prediction model based on the multivariate partial least squares method. Angle Orthod, 92(6):705-713. https://doi.org/10.2319/110121-807.1https://doi.org/10.2319/110121-807.1

Mörch CM, Atsu S, Cai W, et al., 2021. Artificial intelligence and ethics in dentistry: a scoping review. J Dent Res, 100(13):1452-1460. https://doi.org/10.1177/00220345211013808https://doi.org/10.1177/00220345211013808

Moyers RE, Bookstein FL, 1979. The inappropriateness of conventional cephalometrics. Am J Orthod, 75(6):‍599-617. https://doi.org/10.1016/0002-9416(79)90093-9https://doi.org/10.1016/0002-9416(79)90093-9

Nanda SB, Kalha AS, Jena AK, et al., 2015. Artificial neural network (ANN) modeling and analysis for the prediction of change in the lip curvature following extraction and non-extraction orthodontic treatment. J Dent Specialities, 3(2):130-139. https://doi.org/10.5958/2393-9834.2015.00002.9https://doi.org/10.5958/2393-9834.2015.00002.9

Page MJ, McKenzie JE, Bossuyt PM, et al., 2021. The PRISMA 2020 statement: an updated guideline for reporting systematic reviews. BMJ, 372:n71. https://doi.org/10.1136/bmj.n71https://doi.org/10.1136/bmj.n71

Pan YH, 2021. Miniaturized five fundamental issues about vis

ual knowledge. Front Inform Technol Electron Eng, 22(5):615-618. https://doi.org/10.1631/FITEE.2040000https://doi.org/10.1631/FITEE.2040000

Pan YH, 2022. On visual understanding. Front Inform Technol Electron Eng, 23(9):1287-1289. https://doi.org/10.1631/FITEE.2130000https://doi.org/10.1631/FITEE.2130000

Park JH, Kim YJ, Kim J, et al., 2021. Use of artificial intelligence to predict outcomes of nonextraction treatment of Class II malocclusions. Semin Orthod, 27(2):87-95. https://doi.org/10.1053/j.sodo.2021.05.005https://doi.org/10.1053/j.sodo.2021.05.005

Park YS, Choi JH, Kim Y, et al., 2022. Deep learning-based prediction of the 3D postorthodontic facial changes. J Dent Res, 101(11):1372-1379. https://doi.org/10.1177/00220345221106676https://doi.org/10.1177/00220345221106676

Ricketts RM, 1960. Cephalometric synthesis: an exercise in stating objectives and planning treatment with tracings of the head roentgenogram. Am J Orthod, 46(9):647-673. https://doi.org/10.1016/0002-9416(60)90172-Xhttps://doi.org/10.1016/0002-9416(60)90172-X

Rongo R, Bucci R, Adaimo R, et al., 2020. Two-dimensional versus three-dimensional Frӓnkel Manoeuvre: a reproducibility study. Eur J Orthod, 42(2):157-162. https://doi.org/10.1093/ejo/cjz081https://doi.org/10.1093/ejo/cjz081

Ryan R, Hill S, 2016. How to GRADE the quality of the evidence. Cochrane Consumers and Communication Group. http://cccrg.cochrane.org/author-resourceshttp://cccrg.cochrane.org/author-resources

Sample LB, Sadowsky PL, Bradley E, 1998. An evaluation of two VTO methods. Angle Orthod, 68(5):401-408. https://doi.‍org/10.‍1043/0003-3219(1998)068https://doi.‍org/10.‍1043/0003-3219(1998)068<0401:AEOTVM>2.3.CO;2

Scarfe WC, Azevedo B, Toghyani S, et al., 2017. Cone Beam Computed Tomographic imaging in orthodontics. Aust Dent J, 62(Suppl 1):33-50. https://doi.org/10.1111/adj.12479https://doi.org/10.1111/adj.12479

Schwendicke F, Golla T, Dreher M, et al., 2019. Convolutional neural networks for dental image diagnostics: a scoping review. J Dent, 91:103226. https://doi.org/10.1016/j.jdent.2019.103226https://doi.org/10.1016/j.jdent.2019.103226

Shen DG, Wu GR, Suk HI, 2017. Deep learning in medical image analysis. Annu Rev Biomed Eng, 19:221-248. https://doi.org/10.1146/annurev-bioeng-071516-044442https://doi.org/10.1146/annurev-bioeng-071516-044442

Soheilifar S, Soheilifar S, Afrasiabi Z, et al., 2022. Prediction accuracy of Dolphin software for soft-tissue profile in Class I patients undergoing fixed orthodontic treatment. J World Fed Orthod, 11(1):29-35. https://doi.org/10.1016/j.ejwf.2021.10.001https://doi.org/10.1016/j.ejwf.2021.10.001

Stratemann SA, Huang JC, Maki K, et al., 2008. Comparison of cone beam computed tomography imaging with physical measures. Dentomaxillofac Radiol, 37(2):80-93. https://doi.org/10.1259/dmfr/31349994https://doi.org/10.1259/dmfr/31349994

Subramanian AK, Chen Y, Almalki A, et al., 2022. Cephalometric analysis in orthodontics using artificial intelligence ‍– ‍a comprehensive review. Biomed Res Int, 2022:1880113. https://doi.org/10.1155/2022/1880113https://doi.org/10.1155/2022/1880113

Tanikawa C, Yamashiro T, 2021. Development of novel artificial intelligence systems to predict facial morphology after orthognathic surgery and orthodontic treatment in Japanese patients. Sci Rep, 11:15853. https://doi.org/10.1038/s41598-021-95002-whttps://doi.org/10.1038/s41598-021-95002-w

ter Horst R, van Weert H, Loonen T, et al., 2021. Three-dimensional virtual planning in mandibular advancement surgery: soft tissue prediction based on deep learning. J Cranio-Maxillofac Surg, 49(9):775-782. https://doi.org/10.1016/j.jcms.2021.04.001https://doi.org/10.1016/j.jcms.2021.04.001

Toepel-Sievers C, Fischer-Brandies H, 1999. Validity of the computer-assisted cephalometric growth prognosis VTO (Visual treatment objective) according to ricketts. J Orofac Orthop, 60(3):185-194. https://doi.org/10.1007/BF01365265https://doi.org/10.1007/BF01365265

Tong X, 2022. Three-dimensional shape space learning for vis

ual concept construction: challenges and research progress. Front Inform Technol Electron Eng, 23(9):‍1290-1297. https://doi.org/10.1631/FITEE.2200318https://doi.org/10.1631/FITEE.2200318

Vaz JM, Balaji S, 2021. Convolutional neural networks (CNNs): concepts and applications in pharmacogenomics. Mol Divers, 25(3):1569-1584. https://doi.org/10.1007/s11030-021-10225-3https://doi.org/10.1007/s11030-021-10225-3

Wen YF, Wong HM, McGrath CP, 2019. Developmental shape changes in facial morphology: geometric morphometric analyses based on a prospective, population-based, Chinese cohort in Hong Kong. PLoS ONE, 14(6):e0218542. https://doi.org/10.1371/journal.pone.0218542https://doi.org/10.1371/journal.pone.0218542

Whiting PF, Rutjes AWS, Westwood ME, et al., 2011. QUADAS-2: a revised tool for the quality assessment of diagnostic accuracy studies. Ann Intern Med, 155(8):529-536. https://doi.org/10.7326/0003-4819-155-8-201110180-00009https://doi.org/10.7326/0003-4819-155-8-201110180-00009

Zhang X, Mei L, Yan XY, et al., 2019. Accuracy of computer-aided prediction in soft tissue changes after orthodontic treatment. Am J Orthod Dentofacial Orthop, 156(6):‍823-831. https://doi.org/10.1016/j.ajodo.2018.11.021https://doi.org/10.1016/j.ajodo.2018.11.021

Zhang XB, Hu Y, Chen W, et al., 2021. 3D brain glioma segmentation in MRI through integrating multiple densely connected 2D convolutional neural networks. J Zhejiang Univ-Sci B (Biomed & Biotechnol), 22(6):462-475. https://doi.org/10.1631/jzus.B2000381https://doi.org/10.1631/jzus.B2000381

浏览量

Downloads

CSCD

工具集

关联资源

暂无数据