Int J Stomatol ›› 2023, Vol. 50 ›› Issue (4): 491-498.doi: 10.7518/gjkq.2023069
• Reviews • Previous Articles
Zhu Keshi(),Liao Anqi,Yu Youcheng.()
CLC Number:
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. |