Int J Stomatol ›› 2023, Vol. 50 ›› Issue (4): 491-498.doi: 10.7518/gjkq.2023069

• Reviews • Previous Articles    

Research progress on the application of machine learning in dental implantology

Zhu Keshi(),Liao Anqi,Yu Youcheng.()   

  1. Dept. of Stomatology, Zhongshan Hospital Affiliated to Fudan University, Shanghai 200032, China
  • Received:2023-01-12 Revised:2023-04-11 Online:2023-07-01 Published:2023-06-21
  • Contact: Youcheng. Yu E-mail:1329747692@qq.com;yu.youcheng@zs-hospital.sh.cn
  • Supported by:
    National Natural Science Foundation of China(82170990);Major Clinical Research Subproject of Shanghai Municipal Science and Technology Commission(SHDC2020CR2042B);Multicenter Clinical Study of Shanghai Municipal Science and Technology Commission(19411950103)

Abstract:

In recent years, artificial intelligence has been gradually changing the traditional medical industry, and machine learning is the main approach to realize it. In aspect of dental implantation, it can assist doctors in their daily diagnosis and treatment, including intelligent recognition of imaging data, implant scheme optimization, automatic robot and so on. It is the future of dental implantation. This paper summarizes the research progress of machine learning in dental implantation and looks forward to its intellectualization in the future.

Key words: machine learning, artificial intelligence, dental implant

CLC Number: 

  • R 782

TrendMD: 

Tab 1

Comparison of different CNN models applied in recognizing and classifying dental implant systems"

文献CNN模型影像学资料模型评价指标结果结论
[9]SqueezeNet;GoogLeNet;Res-Net-18;MobileNet-v2;ResNet-50801张来源于根尖X线片的单种植体图片准确率、平均精度(average precision,AP)、召回率、F1评分5种模型的测试AP均超过90%;MobileNet-v2和Squee-zeNet的准确率分别约达96%和97%使用参数不到400万的小型网络和少量的图像,CNN也能达较高的精度
[10]CNN(Keras&Tensorflow)1 800张根尖X线片准确率、敏感性、特异性、阳性/阴性预测值(positive predictive value,PPV/ negative predictive value,NPV)、受试者操作特征(receiver operating characteristic,ROC)曲线训练数据组、测试数据组和验证数据的系统准确率分别为99.78%、99.36%和85.29%CNN识别较少种植系统(3个)时可以达到很高的准确率
[11]微调版You Only Look Once(YOLO) v3-1 282张曲面体层片真阳性率(true positive,TP)、AP、平均AP、平均交并比识别每个种植体系统的TP和AP分别为0.50~0.82和0.51~0.85图片质量和样本数量影响了模型对同品牌不同种植系统的识别准确率
[12]CNN-transfer-Inception v31 206张来源于根尖X线片的种植体图片准确率、敏感性、特异性、PPV、NPV、ROC曲线和ROC曲线下面积(area under curve,AUC)诊断准确率93.8%;敏感性为93.5%;特异性为94.2%;PPV为92%深度CNN模型在从X线片识别牙种植体方面有良好的表现。但影像资料数据库规模太小
[13]

基础CNN;VGG16-transfer;微调版VGG16 VGG19-transfer;

微调版VGG19

8 859张来源于曲面体层片的种植体图像准确率、精密度、召回率、ROC、F1评分微调版VGG16和微调版VGG19的准确率分别达0.94和0.93对深度CNN模型的特定卷积块进行微调,深度CNN可以更专门地用于特定分类任务
[14]多任务CNN:ResNet18、34、50、101和1529 767张来源于曲面体层片的种植体图像准确率、AP、召回率、特异性、F1评分、ROC曲线、AUC

多任务模式下,CNN识别准确率均超98%;

多任务模式下,CNN模型的各评价指标都显著高于单任务模式

多任务模式下的迭代学习显著提高了CNN模型的准确性
[15]自动化深层CNN(automate deep CNN, ADCNN):Neuro-T version 2.0.17 146张曲面体层片、4 384张根尖X线片ROC曲线、AUC、Youden指数、敏感性、特异性ADCNN的AUC、Youden指数、敏感性和特异性值分别为0.95、0.81、0.96和0.85ADCNN的表现显著优于大多数参与对照实验的口腔医生
1 Das K, Behera RN. A survey on machine learning: concept, algorithms and applications[J]. Int J Innov Res Comput Commun Eng, 2017, 5: 1301-1309.
2 吴志娜, 胡敏, 宫洵, 等. 机器学习在口腔正畸诊疗中的应用进展[J]. 中华口腔医学杂志, 2021, 101(12): 1277-1281.
Wu ZN, Hu M, Gong X, et al. Application of machine learning in orthodontics[J]. Chin J Stomatol, 2021, 101(12): 1277-1281.
3 汪潇潇, 程兴群. 人工智能在口腔医学领域的应用进展[J]. 实用口腔医学杂志, 2021, 37(5): 710-715.
Wang XX, Cheng XQ. Progress in the application of artificial intelligence in the field of stomatology[J]. J Pract Stomatol, 2021, 37(5): 710-715.
4 刘蓬然, 霍彤彤, 陆林, 等. 人工智能在医学中的应用现状与展望[J]. 中华医学杂志, 2021, 101(44): 3677-3683.
Liu PR, Huo TT, Lu L, et al. The application status and prospects of artificial intelligence in medicine[J]. Natl Med J China, 2021, 101(44): 3677-3683.
5 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.
6 Putra RH, Doi C, Yoda N, et al. Current applications and development of artificial intelligence for digital dental radiography[J]. Dentomaxillofac Radiol, 2022, 51(1): 20210197.
7 Jokstad A, Braegger U, Brunski JB, et al. Quality of dental implants[J]. Int Dent J, 2003, 53(6 ): 409-443.
8 Esposito M, Ardebili Y, Worthington HV. Interventions for replacing missing teeth: different types of dental implants[J]. Cochrane Database Syst Rev, 2014(7): CD003815.
9 Kim JE, Nam NE, Shim JS, et al. Transfer learning via deep neural networks for implant fixture system classification using periapical radiographs[J]. J Clin Med, 2020, 9(4): 1117.
10 da Mata Santos R, Prado H, Neto I, et al. Automated identification of dental implants using artificial intelligence[J]. Int J Oral Maxillofac Implants, 2021, 36(5): 918-923.
11 Takahashi T, Nozaki K, Gonda T, et al. Identification of dental implants using deep learning-pilot study[J]. Int J Implant Dent, 2020, 6(1): 53.
12 Saïd MH, le Roux MK, Catherine JH, et al. Deve-lopment of an artificial intelligence model to identify a dental implant from a radiograph[J]. Int J Oral Maxillofac Implants, 2020, 36(6): 1077-1082.
13 Sukegawa S, Yoshii K, Hara T, et al. Deep neural networks for dental implant system classification[J]. Biomolecules, 2020, 10(7): 984.
14 Sukegawa S, Yoshii K, Hara T, et al. Multi-task deep learning model for classification of dental implant brand and treatment stage using dental pano-ramic radiograph images[J]. Biomolecules, 2021, 11(6): 815.
15 Lee JH, Kim YT, Lee JB, et al. A performance comparison between automated deep learning and dental professionals in classification of dental implant systems from dental imaging: a multi-center study[J]. Diagnostics (Basel), 2020, 10(11): 910.
16 Lee DW, Kim SY, Jeong SN, et al. Artificial intelligence in fractured dental implant detection and classification: evaluation using dataset from two dental hospitals[J]. Diagnostics (Basel), 2021, 11(2): 233.
17 Cha JY, Yoon HI, Yeo IS, et al. Peri-implant bone loss measurement using a region-based convolutio-nal neural network on dental periapical radiographs[J]. J Clin Med, 2021, 10(5): 1009.
18 Liu M, Wang SM, Chen H, et al. A pilot study of a deep learning approach to detect marginal bone loss around implants[J]. BMC Oral Health, 2022, 22(1): 11.
19 Dalago HR, Schuldt Filho G, Rodrigues MA, et al. Risk indicators for periimplantitis. A cross-sectional study with 916 implants[J]. Clin Oral Implan Res, 2017, 28(2): 144-150.
20 El Hage M, Nurdin N, Abi Najm S, et al. Osteotome sinus floor elevation without grafting: a 10-year study of cone beam computerized tomography vs periapical radiography[J]. Int J Periodontics Restora-tive Dent, 2019, 39(3): e89-e97.
21 Kwak GH, Kwak EJ, Song JM, et al. Automatic mandibular canal detection using a deep convolutional neural network[J]. Sci Rep, 2020, 10(1): 5711.
22 Ariji Y, Yanashita Y, Kutsuna S, et al. Automatic detection and classification of radiolucent lesions in the mandible on panoramic radiographs using a deep learning object detection technique[J]. Oral Surg Oral Med Oral Pathol Oral Radiol, 2019, 128(4): 424-430.
23 Orhan K, Bayrakdar IS, Ezhov M, et al. Evaluation of artificial intelligence for detecting periapical pathosis on cone-beam computed tomography scans[J]. Int Endod J, 2020, 53(5): 680-689.
24 Kurt Bayrakdar S, Orhan K, Bayrakdar IS, et al. A deep learning approach for dental implant planning in cone-beam computed tomography images[J]. BMC Med Imaging, 2021, 21(1): 86.
25 Lin Y, Shi M, Xiang D, et al. Construction of an end-to-end regression neural network for the determination of a quantitative index sagittal root inclination[J]. J Periodontol, 2022, 93(12): 1951-1960.
26 Albrektsson T, Zarb G, Worthington P, et al. The long-term efficacy of currently used dental implants: a review and proposed criteria of success[J]. Int J Oral Maxillofac Implants, 1986, 1(1): 11-25.
27 Zhang HG, Shan J, Zhang P, et al. Trabeculae microstructure parameters serve as effective predictors for marginal bone loss of dental implant in the mandible[J]. Sci Rep, 2020, 10(1): 18437.
28 Ha SR, Park HS, Kim EH, et al. A pilot study using machine learning methods about factors influencing prognosis of dental implants[J]. J Adv Prosthodont, 2018, 10(6): 395-400.
29 Liu CH, Lin CJ, Hu YH, et al. Predicting the failure of dental implants using supervised learning techniques[J]. Appl Sci, 2018, 8(5): 698.
30 Papantonopoulos G, Gogos C, Housos E, et al. Prediction of individual implant bone levels and the exis-tence of implant “phenotypes”[J]. Clin Oral Implants Res, 2017, 28(7): 823-832.
31 Khoshkhounejad M, Hashemi Nasab MS, Aminsobhani M, et al. Challenging diagnosis of severe bila-teral cervicofacial subcutaneous emphysema follo-wing root perforation in a maxillary lateral incisor: a case report[J]. Iran Endod J, 2019, 14(3): 220-224.
32 International Surgical Outcomes Study (ISOS) group. Prospective observational cohort study on gra-ding the severity of postoperative complications in global surgery research[J]. Br J Surg, 2019, 106(2): e73-e80.
33 Oliveira ALI, Baldisserotto C, Baldisserotto J. A comparative study on machine learning techniques for prediction of success of dental implants[M]//Lecture notes in computer science. Berlin, Heidelberg: Springer Berlin Heidelberg, 2005: 939-948.
34 Mahri M, Shen N, Berrizbeitia F, et al. Osseointegration pharmacology: a systematic mapping using artificial intelligence[J]. Acta Biomater, 2021, 119: 284-302
35 王岚, 秦思琪, 陈思宇, 等. 三维有限元分析法在口腔种植学中的应用进展[J]. 现代医药卫生, 2022, 38(6): 995-999.
Wang L, Qin SQ, Chen SY, et al. Progress in the application of three-dimensional finite element analysis in dental implant science[J]. J Mod Med Heal, 2022, 38(6): 995-999.
36 Zaw K, Liu GR, Deng B, et al. Rapid identification of elastic modulus of the interface tissue on dental implants surfaces using reduced-basis method and a neural network[J]. J Biomech, 2009, 42(5): 634-641.
37 Roy S, Dey S, Khutia N, et al. Design of patient specific dental implant using FE analysis and computational intelligence techniques[J]. Appl Soft Comput, 2018, 65: 272-279.
38 Li HY, Shi ML, Liu XM, et al. Uncertainty optimization of dental implant based on finite element method, global sensitivity analysis and support vector regression[J]. Proc Inst Mech Eng H, 2019, 233(2): 232-243.
39 Javaid M, Haleem A. Current status and applications of additive manufacturing in dentistry: a literature-based review[J]. J Oral Biol Craniofac Res, 2019, 9(3): 179-185.
40 Türker H, Aksoy B, Özsoy K. Fabrication of Customized dental guide by stereolithography method and evaluation of dimensional accuracy with artificial neural networks[J]. J Mech Behav Biomed Mater, 2022, 126: 105071.
41 Bekeny JR, Ozer E. Transoral robotic surgery frontiers[J]. World J Otorhinolaryngol Head Neck Surg, 2016, 2(2): 130-135.
42 柯怡芳, 张耀鹏, 王勇, 等. 机器人在口腔修复领域的研发及应用现状[J]. 中华口腔医学杂志, 2021(9): 939-944.
Ke YF, Zhang YP, Wang Y, et al. Application and outlook of robotics in prosthetic dentistry[J]. Chin J Stomatol, 2021(9): 939-944.
43 Brief J, Hasβfeld S, Boesecke R, et al. Robot assis-ted dental implantology[J]. Int Poster J Dent Oral Med, 2002, 4(1): 109.
44 Kim G, Seo H, Im S, et al. A study on simulator of human‑robot cooperative manipulator for dental implant surgery[C/OL]//2009 IEEE International Symposium on Industrial Electronics, 2009: 2159‑2164[2021‑07‑14].
45 Sun XY, McKenzie FD, Bawab S, et al. Automated dental implantation using image-guided robotics: registration results[J]. Int J Comput Assist Radiol Surg, 2011, 6(5): 627-634.
46 Syed AA, Mahmood Soomro A, Nighat Khizar A, et al. Tele-robotic assisted dental implant surgery with virtual force feedback[J]. TELKOMNIKA Indonesian J Electr Eng, 2014, 12(1): 450-458.
47 Bolding SL, Reebye UN. Accuracy of haptic robotic guidance of dental implant surgery for completely edentulous arches[J]. J Prosthet Dent, 2022, 128(4): 639-647.
48 白石柱, 赵铱民. 口腔种植机器人的相关研究[C]//第十一次全国口腔修复学学术会议论文汇编, 2017: 147-148.
Bai SZ, Zhao YM. Research on dental implant robots[C]//Compilation of papers from the 11th national conference on prosthodontics, 2017: 147-148.
49 吴秦. 口腔种植机器人空间映射装置的研发及其应用研究[D]. 西安: 第四军医大学, 2016.
Wu Q. Research and application of spatial mapping device for dental implant robot[D]. Xi’an: The Fourth Military Medical University, 2016.
50 白石柱, 任楠, 冯志宏, 等. 自主式口腔种植机器人手术系统动物体内种植精度的研究[J]. 中华口腔医学杂志, 2021, 56(2): 170-174.
Bai SZ, Ren N, Feng ZH, et al. Animal experiment on the accuracy of the autonomous dental implant robotic system[J]. Chin J Stomatol, 2021, 56(2): 170-174.
51 曹正纲. 基于图像引导的颧种植手术机器人系统的研究[D]. 上海: 上海交通大学, 2019.
Cao ZG. Research on image-guided zygomatic implant surgery robot system[D]. Shanghai: Shanghai Jiao Tong University, 2019.
52 Shaheen E, Leite A, Alqahtani KA, et al. A novel deep learning system for multi-class tooth segmentation and classification on cone beam computed tomography. A validation study[J]. J Dent, 2021, 115: 103865.
53 Wang H, Minnema J, Batenburg KJ, et al. Multiclass CBCT image segmentation for orthodontics with deep learning[J]. J Dent Res, 2021, 100(9): 943-949.
54 Hao J, Liao W, Zhang YL, et al. Toward clinically applicable 3-dimensional tooth segmentation via deep learning[J]. J Dent Res, 2022, 101(3): 304-311.
55 Raith S, Vogel EP, Anees N, et al. Artificial Neural Networks as a powerful numerical tool to classify specific features of a tooth based on 3D scan data[J]. Comput Biol Med, 2017, 80: 65-76.
56 Joda T, Gallucci GO, Wismeijer D, et al. Augmented and virtual reality in dental medicine: a systema-tic review[J]. Comput Biol Med, 2019, 108: 93-100.
57 Chen YN, Lee JKY, Kwong G, et al. Morphology and fracture behavior of lithium disilicate dental crowns designed by human and knowledge-based AI[J]. J Mech Behav Biomed Mater, 2022, 131: 105256.
58 Chen QX, Wu J, Li SS, et al. An ontology-driven, case-based clinical decision support model for removable partial denture design[J]. Sci Rep, 2016, 6: 27855.
[1] Han Chong,He Dongning,Yu Feiyan,Wu Dongchao. Research progress on the mechanism and treatment of pain after oral implants [J]. Int J Stomatol, 2024, 51(1): 99-106.
[2] Liao Honglin,Fang Zhonghan,Zhang Yanyan,Liu Fei,Shen Jiefei.. Diagnosis and treatment of post-traumatic trigeminal neuropathic pain after dental implantation [J]. Int J Stomatol, 2023, 50(6): 729-738.
[3] Gong Jiaming,Zhao Ruimin,Pan Hongwei,Lang Xin,Yu Zhanhai,Li Jianxue. Meta-analysis of dynamic navigation versus static navigation in the accuracy of implant surgery [J]. Int J Stomatol, 2023, 50(5): 538-551.
[4] 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.
[5] 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.
[6] Lu Qian,Xia Haibin,Wang Min.. Research progress on implantoplasty in the treatment of peri-implantitis [J]. Int J Stomatol, 2023, 50(2): 152-158.
[7] Man Yi, Huang Dingming. Combined treatment strategy of oral implantology and endodontics microsurgery: clinical protocol and practical cases (part 2) [J]. Int J Stomatol, 2022, 49(6): 621-632.
[8] Man Yi, Huang Dingming. Combined treatment strategy of oral implantology and endodontic microsurgery for bone augmentation and en-dodontic diseases in aesthetic area (part 1): application basis and indications [J]. Int J Stomatol, 2022, 49(5): 497-505.
[9] Luo En. Exploration and clinical application of artificial intelligence in orthognathic surgery [J]. Int J Stomatol, 2022, 49(2): 125-131.
[10] Wang Yue,Wen Bing,Deng Mengting,Li Jianping. Research advances of low-level laser therapy on peri-implant tissue healing [J]. Int J Stomatol, 2021, 48(6): 725-730.
[11] Zhu Xuanzhi,Zhao Lei. Research progress on the relationship between hypothyroidism and periodontitis [J]. Int J Stomatol, 2021, 48(4): 380-384.
[12] Tian Erkang,Xiang Qianrong,Zhao Xinran,Peng Jiahan,Shu Rui. Application of artificial intelligence in oral diagnosis and treatment [J]. Int J Stomatol, 2021, 48(4): 475-484.
[13] Feng Lu,Meng Wenxia. Research progress on the problems of dental implant treatment in patients with common oral mucosal disease [J]. Int J Stomatol, 2021, 48(2): 147-155.
[14] Wang Jia,Li Wenxia,Yin Lihua. Restoration strategy of dental implant for impacted teeth in the edentulous area [J]. Int J Stomatol, 2021, 48(1): 77-81.
[15] Wu Jielin,Gao Ying. Application progress on free soft-tissue grafts harvested from palatal mucosa [J]. Int J Stomatol, 2020, 47(6): 686-692.
Viewed
Full text


Abstract

Cited

  Shared   
  Discussed   
[1] . [J]. Foreign Med Sci: Stomatol, 1999, 26(06): .
[2] . [J]. Foreign Med Sci: Stomatol, 1999, 26(06): .
[3] . [J]. Foreign Med Sci: Stomatol, 1999, 26(06): .
[4] . [J]. Foreign Med Sci: Stomatol, 1999, 26(06): .
[5] . [J]. Foreign Med Sci: Stomatol, 1999, 26(06): .
[6] . [J]. Foreign Med Sci: Stomatol, 1999, 26(06): .
[7] . [J]. Foreign Med Sci: Stomatol, 1999, 26(06): .
[8] . [J]. Foreign Med Sci: Stomatol, 1999, 26(06): .
[9] . [J]. Foreign Med Sci: Stomatol, 1999, 26(06): .
[10] . [J]. Foreign Med Sci: Stomatol, 1999, 26(06): .