Int J Stomatol

Int J Stomatol ›› 2021, Vol. 48 ›› Issue (4): 475-484.doi: 10.7518/gjkq.2021046

• Reviews • Previous Articles     Next Articles

Application of artificial intelligence in oral diagnosis and treatment

Tian Erkang1(),Xiang Qianrong1,Zhao Xinran1,Peng Jiahan1,Shu Rui2()   

  1. 1. State Key Laboratory of Oral Diseases & National Clinical Research Center for Oral Diseases & West China School of Stomatology, Sichuan University, Chengdu 610041, China
    2. State Key Laboratory of Oral Diseases & National Clinical Research Center for Oral Diseases & Dept. of Pediatric Dentistry, West China Hospital of Stomatology, Sichuan University, Chengdu 610041, China
  • Received:2020-08-16 Revised:2020-12-06 Online:2021-07-01 Published:2021-06-30
  • Contact: Rui Shu E-mail:1064564771@qq.com;weis2004@163.com
  • Supported by:
    National Natural Science Foundation of China(81500885)

RichHTML

65

PDF (PC)

970

Abstract:

Artificial intelligence is a subject that makes computer simulate some thinking process and intelligent behavior of human, such as learning, reasoning, thinking, planning, etc.. Since its birth, artificial intelligence has developed rapidly and has been widely used in fields including biomedicine, financial trade and so on, while “artificial intelligence + medical treatment” undertakes the important task of promoting medical progress and changing the status quo of medical treatment. As an important part of medicine, stomatology is characterized by complex diseases and precise operation. Traditional diagnosis and treatment methods have some problems that need to be solved urgently. The application of artificial intelligence in stomatology is committed to solving these problems. This paper reviews the application of artificial intelligence in stomatology and gives a prospect.

Key words: artificial intelligence, oral diagnosis and treatment, intelligent medical treatment

CLC Number: 

  • R78

TrendMD: 

Fig 1

Application of artificial intelligence in oral diagnosis and treatment"

Tab 1

Application of CNN in the field of stomatology"

CNN的类型 应用 样本数量/个 敏感性/% 特异性/% 准确性/% 年份 参考文献
CNN 区分鳞状细胞癌组织与正常组织,甲状腺癌组织与正常组织,用于头颈部癌性组织的诊断 7、13 81.0、93.0 80.0、89.0 81.0、92.0 2018 [9]
完全深度学习掩码区域卷积神经网络（regional con-
volutional neural network,R-CNN）		 检测和定位牙齿结构 1 024 — — 85.8 2020 [10]
16层CNN 检测胶质母细胞瘤和角囊性牙源性肿瘤（keratocystic odontogenic tumor,KCOT） 500 81.8 83.3 83.0 2018 [11]
CNN 在口腔全景片上分割下牙槽神经（inferior alveolar nerve,IAN）与下颌第三磨牙（M3） 81 95.0、83.8 — 94.7、76.8 2019 [12]
12层（7×7×3通道斑块）CNN 口腔黏膜上皮、上皮下、角蛋白等不同组成层的鉴定及角蛋白区内角蛋白珠特征的识别,为临床医生在诊断口腔癌过程中评估组织学图像提供重要帮助 80 97.8 — 96.9 2018 [13]
回归-深度CNN 区分癌组织与正常组织,自动诊断口腔癌 100 94.0 91.0 91.4 2019 [14]
7层前馈CNN 在全景片上检测根尖病变 2 238 65.0 87.0 85.0 2019 [15]
CNN 在锥形束计算机断层扫描（cone beam computed tomography,CBCT）图像上进行3种牙源性囊性病变：牙源性角化囊肿、牙源性囊肿和根尖周囊肿的检测和诊断 2 126 88.2 77.0 84.7 2020 [16]
深度卷积神经网络 识别基于物联网智能医疗系统中的口腔癌区域结构 1 500 92.0 97.0 96.8 2019 [17]
CNN 预测计算机辅助设计和计算机辅助制造（computer aided design and computer aided manufacturing,CAD/CAM）全瓷冠的脱粘接概率 2 160 — — 97.0 2019 [18]
CNN 口腔鳞状细胞癌颈淋巴结转移的术前CT评价 50 92.0 84.0 88.0 2013 [19]
深度学习模型 识别口腔图像中的咬合关系,对咬合面上的龋齿进行检测和分类 79 — — 61.9 2019 [20]
16层CNN 对全景片进行牙齿检测和编号 1 352 98.0 99.9 99.5 2019 [21]
CNN 识别头颈部肿瘤边缘,包括鳞状细胞癌和甲状腺癌 11 84.0、91.0 77.0、88.0 81.0、90.0 2018 [22]

Fig 2

Workflow of the dental implant planning-surgical navigation-surgical navigation-robot control software system"

[1] 毕小琴, 赵佛容. 人工智能技术在口腔专科治疗及护理中的应用[J]. 华西口腔医学杂志, 2018,36(4):452-456.
Bi XQ, Zhao FR. Application of artificial intelligen-ce in stomatology treatment and nursing[J]. West Chi-na J Stomatol, 2018,36(4):452-456.
[2] Rahman HA, Harun SW, Arof H, et al. Classification of reflected signals from cavitated tooth surfa-ces using an artificial intelligence technique incorporating a fiber optic displacement sensor[J]. J Biomed Opt, 2014,19(5):057009.
doi: 10.1117/1.JBO.19.5.057009
[3] Hung K, Montalvao C, Tanaka R, et al. The use and performance of artificial intelligence applications in dental and maxillofacial radiology: a systematic review[J]. Dentomaxillofac Radiol, 2020,49(1):2019-0107.
[4] Lee JH, Kim DH, Jeong SN, et al. Detection and dia-gnosis of dental caries using a deep learning-based convolutional neural network algorithm[J]. J Dent, 2018,77:106-111.
doi: 10.1016/j.jdent.2018.07.015
[5] Lu C, Lewis JS, Dupont WD, et al. An oral cavity squamous cell carcinoma quantitative histomorphometric-based image classifier of nuclear morphology can risk stratify patients for disease-specific survival[J]. Mod Pathol, 2017,30(12):1655-1665.
doi: 10.1038/modpathol.2017.98
[6] Hwang JJ, Jung YH, Cho BH, et al. An overview of deep learning in the field of dentistry[J]. Imaging Sci Dent, 2019,49(1):1-7.
doi: 10.5624/isd.2019.49.1.1
[7] Chen H, Zhang Y, Kalra MK, et al. Low-dose CT with a residual encoder-decoder convolutional neural network[J]. IEEE Trans Med Imaging, 2017,36(12):2524-2535.
doi: 10.1109/TMI.2017.2715284 pmid: 28622671
[8] Wolterink JM, Leiner T, Viergever MA, et al. Gene-rative adversarial networks for noise reduction in low-dose CT[J]. IEEE Trans Med Imaging, 2017,36(12):2536-2545.
doi: 10.1109/TMI.2017.2708987 pmid: 28574346
[9] Halicek M, Little JV, Wang X, et al. Optical biopsy of head and neck cancer using hyperspectral ima-ging and convolutional neural networks[J]. Proc SPIE Int Soc Opt Eng, 2018,10469:104690X.
[10] Lee JH, Han SS, Kim YH, et al. Application of a fully deep convolutional neural network to the automation of tooth segmentation on panoramic radiographs[J]. Oral Surg Oral Med Oral Pathol Oral Radiol, 2020,129(6):635-642.
doi: 10.1016/j.oooo.2019.11.007
[11] Poedjiastoeti W, Suebnukarn S. Application of convolutional neural network in the diagnosis of jaw tumors[J]. Healthc Inform Res, 2018,24(3):236-241.
doi: 10.4258/hir.2018.24.3.236 pmid: 30109156
[12] Vinayahalingam S, Xi T, Bergé S, et al. Automated detection of third molars and mandibular nerve by deep learning[J]. Sci Rep, 2019,9(1):1-7.
[13] Das DK, Bose S, Maiti AK, et al. Automatic identification of clinically relevant regions from oral tissue histological images for oral squamous cell carcinoma diagnosis[J]. Tissue Cell, 2018,53:111-119.
doi: 10.1016/j.tice.2018.06.004
[14] Jeyaraj PR, Samuel Nadar ER. Computer-assisted medical image classification for early diagnosis of oral cancer employing deep learning algorithm[J]. J Cancer Res Clin Oncol, 2019,145(4):829-837.
doi: 10.1007/s00432-018-02834-7
[15] Ekert T, Krois J, Meinhold L, et al. Deep learning for the radiographic detection of apical lesions[J]. J Endod, 2019,45(7): 917-922. e5.
[16] Lee JH, Kim DH, Jeong SN. Diagnosis of cystic lesions using panoramic and cone beam computed tomographic images based on deep learning neural network[J]. Oral Dis, 2020,26(1):152-158.
doi: 10.1111/odi.v26.1
[17] Rajan JP, Rajan SE, Martis RJ, et al. Fog computing employed computer aided cancer classification system using deep neural network in internet of things based healthcare system[J]. J Med Syst, 2019,44(2):34.
doi: 10.1007/s10916-019-1500-5
[18] Yamaguchi S, Lee C, Karaer O, et al. Predicting the debonding of CAD/CAM composite resin crowns with AI[J]. J Dent Res, 2019,98(11):1234-1238.
doi: 10.1177/0022034519867641
[19] Pandeshwar P, Jayanthi K, Raghuram P. Pre-operative contrast enhanced computer tomographic evaluation of cervical nodal metastatic disease in oral squamous cell carcinoma[J]. Indian J Cancer, 2013,50(4):310-315.
doi: 10.4103/0019-509X.123605 pmid: 24369206
[20] Moutselos K, Berdouses E, Oulis C, et al. Recogni-zing occlusal caries in dental intraoral images using deep learning[J]. Annu Int Conf IEEE Eng Med Biol Soc, 2019,2019:1617-1620.
doi: 10.1109/EMBC.2019.8856553 pmid: 31946206
[21] Tuzoff DV, Tuzova LN, Bornstein MM, et al. Tooth detection and numbering in panoramic radiographs using convolutional neural networks[J]. Dentomaxillofac Radiol, 2019,48(4):20180051.
doi: 10.1259/dmfr.20180051
[22] Halicek M, Little JV, Wang X, et al. Tumor margin classification of head and neck cancer using hyperspectral imaging and convolutional neural networks[J]. Proc SPIE Int Soc Opt Eng, 2018,10576:1057-605.
[23] Casalegno F, Newton T, Daher R, et al. Caries detection with near-infrared transillumination using deep learning[J]. J Dent Res, 2019,98(11):1227-1233.
doi: 10.1177/0022034519871884
[24] Lee JH, Kim DH, Jeong SN, et al. Diagnosis and prediction of periodontally compromised teeth using a deep learning-based convolutional neural network algorithm[J]. J Periodontal Implant Sci, 2018,48(2):114-123.
doi: 10.5051/jpis.2018.48.2.114
[25] Lin PL, Huang PW, Huang PY, et al. Alveolar bone-loss area localization in periodontitis radiographs based on threshold segmentation with a hybrid feature fused of intensity and the H-value of fractional Brownian motion model[J]. Comput Methods Programs Biomed, 2015,121(3):117-126.
doi: 10.1016/j.cmpb.2015.05.004 pmid: 26078207
[26] Abdolali F, Zoroofi RA, Otake Y, et al. Automated classification of maxillofacial cysts in cone beam CT images using contourlet transformation and Spherical Harmonics[J]. Comput Methods Programs Bio-med, 2017,139:197-207.
[27] Abdolali F, Zoroofi RA, Otake Y, et al. Automatic segmentation of maxillofacial cysts in cone beam CT images[J]. Comput Biol Med, 2016,72:108-119.
doi: 10.1016/j.compbiomed.2016.03.014
[28] Rana M, Modrow D, Keuchel J, et al. Development and evaluation of an automatic tumor segmentation tool: a comparison between automatic, semi-automa-tic and manual segmentation of mandibular odontogenic cysts and tumors[J]. J Craniomaxillofac Surg, 2015,43(3):355-359.
doi: 10.1016/j.jcms.2014.12.005
[29] Ashizawa K, Yoshimura K, Johno H, et al. Construction of mass spectra database and diagnosis algori-thm for head and neck squamous cell carcinoma[J]. Oral Oncol, 2017,75:111-119.
doi: 10.1016/j.oraloncology.2017.11.008
[30] 陈青筱, 吕培军. 临床决策支持系统的研究现状及其在口腔修复领域的应用[J]. 实用口腔医学杂志, 2016,32(5):722-726.
Chen QX, Lü PJ. Research status of clinical decision support system and its application in the field of pro-sthodontics[J]. J Pract Stomatol, 2016,32(5):722-726.
[31] 吕培军, 李国珍, 王毓英, 等. 计算机辅助可摘式局部义齿设计的专家系统[J]. 中华口腔医学杂志, 1993,28(1):9-11.
Lü PJ, Li GZ, Wang YY, et al. The expert system of computer aided design of removable partial denture[J]. Chin J Stomatol, 1993,28(1):9-11.
[32] 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.2018.27.issue-8
[33] Aliaga IJ, Vera V, De Paz JF, et al. Modelling the longevity of dental restorations by means of a CBR system[J]. Biomed Res Int, 2015,2015:540306.
[34] Weiss R 2nd, Read-Fuller A. Cone beam computed tomography in oral and maxillofacial surgery: an evidence-based review[J]. Dent J (Basel), 2019,7(2):52.
[35] Chiarelli T, Franchini F, Lamma A, et al. From implant planning to surgical execution: an integrated approach for surgery in oral implantology[J]. Int J Med Robot, 2012,8(1):57-66.
doi: 10.1002/rcs.422
[36] Fortin T, Camby E, Alik M, et al. Panoramic images versus three-dimensional planning software for oral implant planning in atrophied posterior maxillary: a clinical radiological study[J]. Clin Implant Dent Relat Res, 2013,15(2):198-204.
doi: 10.1111/cid.2013.15.issue-2
[37] Takada K. Artificial intelligence expert systems with neural network machine learning may assist decision-making for extractions in orthodontic treatment planning[J]. J Evid Based Dent Pract, 2016,16(3):190-192.
doi: S1532-3382(16)30095-1 pmid: 27855838
[38] 赵越, 陆正福, 于兵. 计算机辅助口腔正畸诊断系统的研究[J]. 计算机与数字工程, 2008,36(4):44-46.
Zhao Y, Lu ZF, Yu B. Study on the expert system by computer-aided orthodontic diagnosis[J]. Comput Di-git Eng, 2008,36(4):44-46.
[39] Noroozi H. Orthodontic treatment planning software[J]. Am J Orthod Dentofac Orthop, 2006,129(6):834-837.
doi: 10.1016/j.ajodo.2006.02.025
[40] 申龙朵, 段世均, 汤炜. 专家系统在口腔医学领域中的应用进展[J]. 现代生物医学进展, 2012,12(13):2585-2587.
Shen LD, Duan SJ, Tang W. Application progress of the expert system in dentistry[J]. Prog Mod Biomed, 2012,12(13):2585-2587.
[41] Mago VK, Mago A, Sharma P, et al. Fuzzy logic based expert system for the treatment of mobile tooth[J]. Adv Exp Med Biol, 2011,696:607-614.
[42] Machado JP, Lam XT, Chen JW. Use of a clinical decision support tool for the management of traumatic dental injuries in the primary dentition by no-vice and expert clinicians[J]. Dent Traumatol, 2018,34(2):120-128.
doi: 10.1111/edt.2018.34.issue-2
[43] Alarifi A, AlZubi AA. Memetic search optimization along with genetic scale recurrent neural network for predictive rate of implant treatment[J]. J Med Syst, 2018,42(11):202.
doi: 10.1007/s10916-018-1051-1
[44] Peterman RJ, Jiang S, Johe R, et al. Accuracy of Dolphin visual treatment objective (VTO) prediction software on Class Ⅲ patients treated with maxillary advancement and mandibular setback[J]. Prog Orthod, 2016,17(1):19.
doi: 10.1186/s40510-016-0132-2 pmid: 27312722
[45] 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
[46] Pereira KR, Sinha R. Welcome the “new kid on the block” into the family: artificial intelligence in oral and maxillofacial surgery[J]. Br J Oral Maxillofac Surg, 2020,58(1):83-84.
doi: 10.1016/j.bjoms.2019.08.011
[47] Bur AM, Holcomb A, Goodwin S, et al. Machine learning to predict occult nodal metastasis in early oral squamous cell carcinoma[J]. Oral Oncol, 2019,92:20-25.
doi: 10.1016/j.oraloncology.2019.03.011
[48] Gui H, Yang H, Shen SG, et al. Image-guided surgical navigation for removal of foreign bodies in the deep maxillofacial region[J]. J Oral Maxillofac Surg, 2013,71(9):1563-1571.
doi: 10.1016/j.joms.2013.04.001
[49] Li P, Xuan M, Liao CH, et al. Application of intraope-rative navigation for the reconstruction of mandibular defects with microvascular fibular flaps-preliminary clinical experiences[J]. J Craniofacial Surg, 2016,27(3):751-755.
doi: 10.1097/SCS.0000000000002430
[50] Essig H, Dressel L, Rana M, et al. Precision of posttraumatic primary orbital reconstruction using individually bent titanium mesh with and without navigation: a retrospective study[J]. Head Face Med, 2013,9:18.
doi: 10.1186/1746-160X-9-18
[51] Jiang W, Ma L, Zhang B, et al. Evaluation of the 3D augmented reality-guided intraoperative positioning of dental implants in edentulous mandibular models[J]. Int J Oral Maxillofac Implants, 2018,33(6):1219-1228.
doi: 10.11607/jomi.6638
[52] Suenaga H, Hoang Tran H, Liao H, et al. Real-time in situ three-dimensional integral videography and surgical navigation using augmented reality: a pilot study[J]. Int J Oral Sci, 2013,5(2):98-102.
doi: 10.1038/ijos.2013.26
[53] Yu H, Wang X, Zhang S, et al. Navigation-guided en bloc resection and defect reconstruction of craniomaxillary bony tumours[J]. Int J Oral Maxillofac Surg, 2013,42(11):1409-1413.
doi: 10.1016/j.ijom.2013.05.011
[54] Gui HJ, Wu JY, Shen SGF, et al. Navigation-guided lateral gap arthroplasty as the treatment of temporomandibular joint ankylosis[J]. J Oral Maxillofac Surg, 2014,72(1):128-138.
doi: 10.1016/j.joms.2013.07.039
[55] Gui HJ, Zhang SL, Shen SGF, et al. Real-time ima-ge-guided recontouring in the management of craniofacial fibrous dysplasia[J]. Oral Surg Oral Med Oral Pathol Oral Radiol, 2013,116(6):680-685.
doi: 10.1016/j.oooo.2013.07.012
[56] Zhang YD, Zhao ZF, Song RJ, et al. Tooth arrangement for the manufacture of a complete denture u-sing a robot[J]. Ind Robot, 2001,28(5):420-425.
doi: 10.1108/01439910110401286
[57] Shi Y, Lin L, Zhou C, et al. A study of an assisting robot for mandible plastic surgery based on augmented reality[J]. Minim Invasive Ther Allied Technol, 2017,26(1):23-30.
doi: 10.1080/13645706.2016.1216864
[58] Holsinger FC. A flexible, single-arm robotic surgical system for transoral resection of the tonsil and lateral pharyngeal wall: next-generation robotic head and neck surgery[J]. Laryngoscope, 2016,126(4):864-869.
doi: 10.1002/lary.25724
[59] Theodossy T, Bamber MA. Model surgery with a passive robot arm for orthognathic surgery planning[J]. J Oral Maxillofac Surg, 2003,61(11):1310-1317.
doi: 10.1016/S0278-2391(03)00733-X
[60] 陈黎明, 栾楠, 张诗雷, 等. 颅颌面骨畸形整复手术中辅助机器人的应用[J]. 机械与电子, 2010,28(4):57-60.
Chen LM, Luan N, Zhang SL, et al. Research on the application of multi DOF robot on orthognathic navi-gation surgery[J]. Mach Electron, 2010,28(4):57-60.
[61] Khan K, Dobbs T, Swan MC, et al. Trans-oral robo-tic cleft surgery (TORCS) for palate and posterior pharyngeal wall reconstruction: a feasibility study[J]. J Plast Reconstr Aesthet Surg, 2016,69(1):97-100.
doi: 10.1016/j.bjps.2015.08.020
[62] Omar EA, Bamber MA. Orthognathic model surgery by using of a passive Robot Arm[J]. Saudi Dent J, 2010,22(2):47-55.
doi: 10.1016/j.sdentj.2010.02.006
[63] Nadjmi N. Transoral robotic cleft palate surgery[J]. Cleft Palate Craniofac J, 2016,53(3):326-331.
doi: 10.1597/14-077
[64] Kayhan FT, Kaya H, Yazici ZM. Transoral robotic surgery for tongue-base adenoid cystic carcinoma[J]. J Oral Maxillofac Surg, 2011,69(11):2904-2908.
doi: 10.1016/j.joms.2011.01.049
[65] Walvekar RR, Peters G, Hardy E, et al. Robotic-assisted transoral removal of a bilateral floor of mouth ranulas[J]. World J Surg Oncol, 2011,9:78.
doi: 10.1186/1477-7819-9-78 pmid: 21767364
[66] Genden EM, Desai S, Sung CK. Transoral robotic surgery for the management of head and neck cancer: a preliminary experience[J]. Head Neck, 2009,31(3):283-289.
doi: 10.1002/hed.v31:3
[67] 史俊斌. 世界首台自主式种植牙手术机器人在西安问世[J]. 人人健康, 2017(20):49.
Shi JB. The world’s first autonomous implant sur-gery robot came out in Xi’an[J]. Health Everyone, 2017(20):49.
[68] Ma Q, Kobayashi E, Wang J, et al. Development and preliminary evaluation of an autonomous surgical sy-stem for oral and maxillofacial surgery[J]. Int J Med Robot, 2019,15(4):e1997.
[69] 王芳. 精准医学时代下的精准临床护理定位的思考[J]. 全科护理, 2017,15(9):1043-1045.
Wang F. Thinking on precise clinical nursing posi-tioning in the era of precise medicine[J]. Chin Gen Pract Nurs, 2017,15(9):1043-1045.
[70] Scheerman JFM, van Meijel B, van Empelen P, et al. The effect of using a mobile application (“WhiteTeeth”) on improving oral hygiene: a randomized controlled trial[J]. Int J Dent Hyg, 2020,18(1):73-83.
doi: 10.1111/idh.12415 pmid: 31291683
[71] Fu SW, Li PC, Lai YH, et al. Joint dictionary lear-ning-based non-negative matrix factorization for voi-ce conversion to improve speech intelligibility after oral surgery[J]. IEEE Trans Biomed Eng, 2017,64(11):2584-2594.
doi: 10.1109/TBME.10
[72] Mishra R, Burke A, Gitman B, et al. Data-driven method to enhance craniofacial and oral phenotype vocabularies[J]. J Am Dent Assoc, 2019,150(11): 933-939. e2.
doi: 10.1016/j.adaj.2018.06.020
[1] Zhu Keshi,Liao Anqi,Yu Youcheng.. Research progress on the application of machine learning in dental implantology [J]. Int J Stomatol, 2023, 50(4): 491-498.
[2] Lin Huiping,Xu Ting,Lin Jun.. Research progress on artificial intelligence techniques in diagnosis of oral cancer and potentially malignant disorders [J]. Int J Stomatol, 2023, 50(2): 138-145.
[3] Tang Yueting,Dai Jiaqi,Dong Wenxuan,Wang Hu,Guo Jixiang,You Meng. Research progress of dental age evaluation based on machine learning methods [J]. Int J Stomatol, 2023, 50(2): 146-151.
[4] Luo En. Exploration and clinical application of artificial intelligence in orthognathic surgery [J]. Int J Stomatol, 2022, 49(2): 125-131.
[5] Yang Jiazhen,Zhang Ying,Liu Yuhan,Li Fan,Zeng Fei,Li Xiuzhen,Ma Yuying,Yang Fang. Time variation trend of bacterial community in oral diagnosis and treatment environment [J]. Int J Stomatol, 2022, 49(2): 132-137.
Viewed
Full text


Abstract

Cited
  Shared   
  Discussed   
[1] . [J]. Foreign Med Sci: Stomatol, 1999, 26(06): .
[2] . [J]. Foreign Med Sci: Stomatol, 1999, 26(05): .
[3] . [J]. Foreign Med Sci: Stomatol, 1999, 26(05): .
[4] . [J]. Foreign Med Sci: Stomatol, 1999, 26(05): .
[5] . [J]. Foreign Med Sci: Stomatol, 1999, 26(05): .
[6] . [J]. Foreign Med Sci: Stomatol, 1999, 26(04): .
[7] . [J]. Foreign Med Sci: Stomatol, 2005, 32(06): 458 -460 .
[8] . [J]. Foreign Med Sci: Stomatol, 2005, 32(06): 452 -454 .
[9] . [J]. Inter J Stomatol, 2008, 35(S1): .
[10] . [J]. Inter J Stomatol, 2008, 35(S1): .