-
摘要: 颜面部具有呼吸、语言、面部表情等多项功能,其发育是一个复杂而漫长的过程,会受到遗传、疾病、不良习惯以及外伤等多种因素的影响。早发现、早诊断、早治疗是儿童颜面管理的重要理念。数字化技术医学是一门新兴技术,可为儿童颜面管理带来极大好处。本文就数字化技术在儿童颜面管理方面的研究进行综述,并聚焦于儿童阻塞性睡眠呼吸暂停、错
畸形、唇腭裂等相关疾病研究并进行一综述。Abstract: The maxillofacial region has multiple functions such as breathing, language, and facial expressions. Children's maxillofacial development is a complex and long process, which is affected by many factors such as genetics, diseases, bad habits and trauma. Early detection, early diagnosis, and early treatment are important concepts in children's maxillofacial management. Digital technology medicine is an emerging technology based on medical imaging and anatomy that has emerged in recent years. The application of this technology in the field of clinical medicine will undoubtedly bring great benefits to children's maxillofacial management. This article summarizes the research on digital technology in children's maxillofacial management, and focuses on the research on children's malocclusion, children's OSA, cleft lip and palate and other related diseases. -
[1] Alsufyani NA, Flores-Mir C, Major PW. Three-dimensional segmentation of the upper airway using cone beam CT: a systematic review[J]. Dentomaxillofac Radiol, 2012, 41(4): 276-284. doi: 10.1259/dmfr/79433138
[2] Zhang C, Bruggink R, Baan F, et al. A new segmentation algorithm for measuring CBCT images of nasal airway: a pilot study[J]. PeerJ, 2019, 7: e6246. doi: 10.7717/peerj.6246
[3] Mupparapu M, Shi KJ, Lo AD, et al. Novel 3D segmentation for reliable volumetric assessment of the nasal airway: a CBCT study[J]. Quintessence Int, 2021, 52(2): 154-164.
[4] Kamaruddin N, Daud F, Yusof A, et al. Comparison of automatic airway analysis function of Invivo5 and Romexis software[J]. Peer J, 2019, 7: e6319. doi: 10.7717/peerj.6319
[5] Fonseca C, Cavadas F, Fonseca P. Upper Airway Assessment in Cone-Beam Computed Tomography for Screening of Obstructive Sleep Apnea Syndrome: Development of an Evaluation Protocol in Dentistry[J]. JMIR Res Protoc, 2023, 12: e41049. doi: 10.2196/41049
[6] 唐媛媛, 孙秀珍, 刘迎曦, 等, 腺样体肥大患儿上气道气流流场模型的建立与数值分析[J]. 中国耳鼻咽喉头颈外科, 2012, 19(3): 155-158. https://www.cnki.com.cn/Article/CJFDTOTAL-EBYT201203015.htm
[7] 王宏伟, 齐素青, 刘朝兵, 等, 腺样体肥大患者上气道计算流体动力学模型的构建及数值模拟[J]. 中华口腔医学杂志, 2023, 58(4): 337-344. doi: 10.3760/cma.j.cn112144-20221024-00556
[8] Zhao T, Zhou J, Yan J, et al. Automated Adenoid Hypertrophy Assessment with Lateral Cephalometry in Children Based on Artificial Intelligence[J]. Diagnostics(Basel), 2021, 11(8): 1386.
[9] Lam B, Ip MS, Tench E, et al. Craniofacial profile in Asian and white subjects with obstructive sleep apnoea[J]. Thorax, 2005, 60(6): 504-510. doi: 10.1136/thx.2004.031591
[10] Tabatabaei Balaei A, Sutherland K, Cistulli P, et al. Prediction of obstructive sleep apnea using facial landmarks[J]. Physiol Meas, 2018, 39(9): 094004. doi: 10.1088/1361-6579/aadb35
[11] Espinoza-Cuadros F, Fernández-Pozo R, Toledano DT, et al. Speech Signal and Facial Image Processing for Obstructive Sleep Apnea Assessment[J]. Comput Math MethodsMed, 2015, 2015: 489761.
[12] Wang H, Xu W, Zhao A, et al. Clinical Characteristics Combined with Craniofacial Photographic Analysis in Children with Obstructive Sleep Apnea[J]. Nat Sci Sleep, 2023, 15: 115-125. doi: 10.2147/NSS.S400745
[13] Kühlman DC, Almuzian M, Coppini C, et al. Accuracy(trueness and precision)of four tablet-based applications for three-dimensional facial scanning: An in-vitro study[J]. J Dent, 2023, 135: 104533. doi: 10.1016/j.jdent.2023.104533
[14] Lin SW, Sutherland K, Liao YF, et al. Three-dimensional photography for the evaluation of facial profiles in obstructive sleep apnoea[J]. Respirology, 2018, 23(6): 618-625. doi: 10.1111/resp.13261
[15] Ohmura K, Suzuki M, Soma M, et al. Predicting the presence and severity of obstructive sleep apnea based on mandibular measurements using quantitative analysis of facial profiles via three-dimensional photogrammetry[J]. Respir Investig, 2022, 60(2): 300-308. doi: 10.1016/j.resinv.2021.10.002
[16] Eastwood P, Gilani SZ, McArdle N, et al. Predicting sleep apnea from three-dimensional face photography[J]. J Clin Sleep Med, 2020, 16(4): 493-502. doi: 10.5664/jcsm.8246
[17] Banumathi A, Raju S, Abhaikumar V. Diagnosis of dental deformities in cephalometry images using support vector machine[J]. J Med Syst, 2011, 35(1): 113-119. doi: 10.1007/s10916-009-9347-9
[18] Lee JH, Yu HJ, Kim MJ, et al. Automated cephalometric landmark detection with confidence regions using Bayesian convolutional neural networks[J]. BMC Oral Health, 2020, 20(1): 270. doi: 10.1186/s12903-020-01256-7
[19] Kim H, Shim E, Park J, et al. Web-based fully automated cephalometric analysis by deep learning[J]. Comput MethodsPrograms Biomed, 2020, 194: 105513.
[20] Kim J, Kim I, Kim YJ, et al. Accuracy of automated identification of lateral cephalometric landmarks using cascade convolutional neural networks on lateral cephalograms from nationwide multi-centres[J]. Orthod Craniofac Res, 2021, 2: 59-67.
[21] Schwendicke F, Chaurasia A, Arsiwala L, et al. Deep learning for cephalometric landmark detection: systematic review and meta-analysis[J]. Clin Oral Investig, 2021, 25(7): 4299-4309. doi: 10.1007/s00784-021-03990-w
[22] Sukun T, Ning D, Bei Z, et al. Automatic Classification and Segmentation of Teeth on 3D Dental Model Using Hierarchical Deep Learning Networks[J]. IEEE Access, 2019, 7: 84817-84828. doi: 10.1109/ACCESS.2019.2924262
[23] Prasad J, Mallikarjunaiah DR, Shetty A, et al. Machine Learning Predictive Model as Clinical Decision Support System in Orthodontic Treatment Planning[J]. Dent J(Basel), 2022, 11(1): 1.
[24] Jung SK, Kim TW. New approach for the diagnosis of extractions with neural network machine learning[J]. Am J Orthod Dentofacial Orthop, 2016, 149(1): 127-133. doi: 10.1016/j.ajodo.2015.07.030
[25] Thanathornwong B. Bayesian-Based Decision Support System for Assessing the Needs for Orthodontic Treatment[J]. Healthc Inform Res, 2018, 24(1): 22-28. doi: 10.4258/hir.2018.24.1.22
[26] Suhail Y, Upadhyay M, Chhibber A, et al. Machine Learning for the Diagnosis of Orthodontic Extractions: A Computational Analysis Using Ensemble Learning[J]. Bioengineering(Basel), 2020, 7(2): 55.
[27] AlAli AB, Griffin MF, Calonge WM, et al. Evaluating the Use of Cleft Lip and Palate 3D-Printed Models as a Teaching Aid[J]. J Surg Educ, 2018, 75(1): 200-208. doi: 10.1016/j.jsurg.2017.07.023
[28] Levaillant JM, Nicot R, Benouaiche L, et al. Prenatal diagnosis of cleft lip/palate: The surface rendered oro-palatal(SROP)view of the fetal lips and palate, a tool to improve information-sharing within the orofacial team and with the parents[J]. J Craniomaxillofac Surg, 2016, 44(7): 835-842. doi: 10.1016/j.jcms.2016.04.006
[29] Bous RM, Kochenour N, Valiathan M. A novel method for fabricating nasoalveolar molding appliances for infants with cleft lip and palate using 3-dimensional workflow and clear aligners[J]. Am J Orthod Dentofacial Orthop, 2020, 158(3): 452-458. doi: 10.1016/j.ajodo.2020.02.007
[30] Wang Y, Zhang Z, Liu Y, et al. Virtual Surgical Planning Assisted Management for Cleft-Related Maxillary Hypoplasia[J]. J Craniofac Surg, 2019, 30(6): 1745-1749. doi: 10.1097/SCS.0000000000005603
[31] Wang Y, Li J, Xu Y, et al. Accuracy of virtual surgical planning-assisted management for maxillary hypoplasia in adult patients with cleft lip and palate[J]. J Plast Reconstr Aesthet Surg, 2020, 73(1): 134-140. doi: 10.1016/j.bjps.2019.07.003
[32] Choi YS, Shin HS. Preoperative Planning and Simulation in Patients With Cleft Palate Using Intraoral Three-Dimensional Scanning and Printing[J]. J Craniofac Surg, 2019, 30(7): 2245-2248. doi: 10.1097/SCS.0000000000005983
[33] Pálházi P, Nemes B, Swennen G, et al. Three-dimensional simulation of the nasoalveolar cleft defect[J]. Cleft Palate Craniofac J, 2014, 51(5): 593-596. doi: 10.1597/13-041
[34] Luo D, Li T, Wang H, et al. Three-Dimensional Printing of Personalized Nasal Stents for Patients With Cleft Lip[J]. Cleft Palate Craniofac J, 2019, 56(4): 521-524. doi: 10.1177/1055665618782804
[35] Jung JW, Ha DH, Kim BY, et al. Nasal Reconstruction Using a Customized Three-Dimensional-Printed Stent for Congenital Arhinia: Three-Year Follow-up[J]. Laryngoscope, 2019, 129(3): 582-585. doi: 10.1002/lary.27335
[36] Brézulier D, Chaigneau L, Jeanne S, et al. The Challenge of 3D Bioprinting of Composite Natural Polymers PLA/Bioglass: Trends and Benefits in Cleft Palate Surgery[J]. Biomedicines, 2021, 9(11): 1553. doi: 10.3390/biomedicines9111553
[37] Ahn G, Lee JS, Yun WS, et al. Cleft Alveolus Reconstruction Using a Three-Dimensional Printed Bioresorbable Scaffold With Human Bone Marrow Cells[J]. J Craniofac Surg, 2018, 29(7): 1880-1883. doi: 10.1097/SCS.0000000000004747
[38] Boyer CJ, Woerner JE, Galea C, et al. Personalized Bioactive Nasal Supports for Postoperative Cleft Rhinoplasty[J]. J Oral Maxillofac Surg, 2018, 76(7): 1562.e1-1562.
[39] Chapman KL, Baylis A, Trost-Cardamone J, et al. The Americleft Speech Project: A Training and Reliability Study[J]. Cleft Palate Craniofac J, 2016, 53(1): 93-108. doi: 10.1597/14-027
[40] 郭毅波, 蔡鸣. 计算机科学应用于唇腭裂语音诊疗的研究进展[J]. 口腔疾病防治, 2022, 30(6): 453-456. https://www.cnki.com.cn/Article/CJFDTOTAL-GDYB202206012.htm
[41] Nakai Y, Takiguchi T, Matsui G, et al. Detecting Abnormal Word Utterances in Children With Autism Spectrum Disorders: Machine-Learning-Based Voice Analysis Versus Speech Therapists[J]. Percept Mot Skills, 2017, 124(5): 961-973.
[42] Lee LM, Le HH, Jean FR. Improved model adaptation approach for recognition of reduced-frame-rate continuous speech[J]. PLoS One, 2018, 13(11): e0206916.
[43] 何凌, 何飞, 王熙月, 等. 基于多延迟四阶累积量倍频程谱线的腭裂语音咽擦音自动检测算法[J]. 计算机科学, 2020, 47(1): 144-152. https://www.cnki.com.cn/Article/CJFDTOTAL-JSJA202001019.htm
[44] 付佳, 田婷, 唐铭, 等. 结合PECGTFs和SSMC的腭裂语音咽擦音自动检测算法[J]. 计算机工程与应用, 2019, 5(24): 102-109. https://www.cnki.com.cn/Article/CJFDTOTAL-JSGG201924016.htm
计量
- 文章访问数: 1073
- PDF下载数: 257
- 施引文献: 0