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ACOMS+ 및 학술지 리포지터리 설명회

  • 한국과학기술정보연구원(KISTI) 서울분원 대회의실(별관 3층)
  • 2024년 07월 03일(수) 13:30
 

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4차 산업혁명과 턱교정수술

4th industrial revolution in Orthognahtic surgery

Abstract

Historically, the emergence of new technologies has had a major impact on industries as a whole. The field of orthodontic surgery was no different, especially with the advent of computers in the 20th century, oral and maxillofacial surgeons and orthodontists were able to make more accurate and precise diagnoses, resulting in improved results of double jaw surgery. In this manuscript, I will introduce some studies on how robots and artificial intelligence, which are said to be two new technologies in the era of the 4th industrial revolution, are being applied to the field of orthognathic surgery.

keywords
Orthognathic surgery, Artificial intelligence, Robot, 4th industrial revolution.

참고문헌

1.

1. Han JJ, Yang HJ, Hwang SJ. Repositioning of the Maxillomandibular Complex Using Maxillary Template Adjusted Only by Maxillary Surface Configuration Without an Intermediate Splint in Orthognathic Surgery. J Craniofac Surg. 2016;27(6):1550-1553.

2.

2. Zinser MJ, Sailer HF, Ritter L, Braumann B, Maegele M, Zoller JE. A paradigm shift in orthognathic surgery? A comparison of navigation, computer-aided designed/computer-aided manufactured splints, and "classic" intermaxillary splints to surgical transfer of virtual orthognathic planning. J Oral Maxillofac Surg. 2013;71(12):2151e2151-2121.

3.

3. Aboul-Hosn Centenero S, Hernandez-Alfaro F. 3D planning in orthognathic surgery: CAD/CAM surgical splints and prediction of the soft and hard tissues results - our experience in 16 cases. J Craniomaxillofac Surg. 2012;40(2):162-168.

4.

4. Chen X, Li X, Xu L, Sun Y, Politis C, Egger J. Development of a computer-aided design software for dental splint in orthognathic surgery. Sci Rep. 2016;6:38867.

5.

5. Li B, Wei H, Zeng F, Li J, Xia JJ, Wang X. Application of A Novel Three-dimensional Printing Genioplasty Template System and Its Clinical Validation: A Control Study. Sci Rep. 2017;7(1):5431.

6.

6. Ellis E, 3rd. Accuracy of model surgery: evaluation of an old technique and introduction of a new one. J Oral Maxillofac Surg. 1990;48(11):1161-1167.

7.

7. Li B, Zhang L, Sun H, Yuan J, Shen SG, Wang X. A novel method of computer aided orthognathic surgery using individual CAD/CAM templates: a combination of osteotomy and repositioning guides. Br J Oral Maxillofac Surg. 2013;51(8):e239-244.

8.

8. Olszewski R, Reychler H. [Limitations of orthognathic model surgery:theoretical and practical implications]. Rev Stomatol Chir Maxillofac. 2004;105(3):165-169.

9.

9. Bai S, Shang H, Liu Y, Zhao J, Zhao Y. Computer-aided design and computer-aided manufacturing locating guides accompanied with prebent titanium plates in orthognathic surgery. J Oral Maxillofac Surg. 2012;70(10):2419-2426.

10.

10. Kahnberg KE, Sunzel B, Astrand P. Planning and control of vertical dimension in Le Fort I osteotomies. J Craniomaxillofac Surg. 1990;18(6):267-270.

11.

11. Peters BS, Armijo PR, Krause C, Choudhury SA, Oleynikov D. Review of emerging surgical robotic technology. Surg Endosc. 2018Apr;32(4):1636-1655. doi: 10.1007/s00464-018-6079-2. Epub 2018 Feb 13. PMID: 29442240.

12.

12. Theodossy T, Bamber MA. Model surgery with a passive robot arm for orthognathic surgery planning. J Oral Maxillofac Surg. 2003 Nov;61(11):1310-7. doi: 10.1016/s0278-2391(03)00733-x. PMID: 14613088.

13.

13. Ferguson JW, Luyk NH. Control of vertical dimension during maxillary orthognathic surgery. A clinical trial comparing internal and external fixed reference points. J Craniomaxillofac Surg. 1992;20(8):333-336.

14.

14. Suojanen J, Leikola J, Stoor P. The use of patient-specific implants in orthognathic surgery: A series of 32 maxillary osteotomy patients. J Craniomaxillofac Surg. 2016;44(12):1913-1916.

15.

15. Gander T, Bredell M, Eliades T, Rucker M, Essig H. Splintless orthognathic surgery: a novel technique using patient-specific implants (PSI). J Craniomaxillofac Surg. 2015;43(3):319-322.

16.

16. Mazzoni S, Bianchi A, Schiariti G, Badiali G, Marchetti C. Computeraided design and computer-aided manufacturing cutting guides and customized titanium plates are useful in upper maxilla waferless repositioning. J Oral Maxillofac Surg. 2015;73(4):701-707.

17.

17. Polley JW, Figueroa AA. Orthognathic positioning system: intraoperative system to transfer virtual surgical plan to operating field during orthognathic surgery. J Oral Maxillofac Surg. 2013;71(5):911-920.

18.

18. Brunso J, Franco M, Constantinescu T, Barbier L, Santamaria JA, Alvarez J. Custom-Machined Miniplates and Bone-Supported Guides for Orthognathic Surgery: A New Surgical Procedure. J Oral Maxillofac Surg. 2016;74(5):1061 e1061-1061 e1012.

19.

19. Woo SY, Lee SJ, Yoo JY, et al. Autonomous bone reposition around anatomical landmark for robot-assisted orthognathic surgery. J Craniomaxillofac Surg. 2017;45(12):1980-1988.

20.

20. Suojanen J, Leikola J, Stoor P. The use of patient-specific implants in orthognathic surgery: A series of 30 mandible sagittal split osteotomy patients. J Craniomaxillofac Surg. 2017;45(6):990-994.

21.

21. Chapuis J SA, Pappas I, et al. A new system for computer-aided preoperative planning and intraoperative navigation during corrective jaw surgery. IEEE Trans Inf Technol Biomed 2007;2007;11:274-287.

22.

22. Lee SJ, Woo SY, Huh KH, et al. Virtual skeletal complex model- and landmark-guided orthognathic surgery system. J Craniomaxillofac Surg. 2016;44(5):557-568.

23.

23. Berger M, Nova I, Kallus S, et al. Can electromagnetic-navigated maxillary positioning replace occlusional splints in orthognathic surgery? A clinical pilot study. J Craniomaxillofac Surg. 2017;45(10):1593-1599.

24.

24. Beasley RA. Medical Robots: Current Systems and Research Directions. Journal of Robotics. 2012;Volume 2012, Article ID 401613, 14 pages.

25.

25. Wang X SR, Liu X, et al. System design for orthognathic aided robot. In: The 5th annual IEEE International Conference on cyber technology in automation, control and intelligent systems. 2015.

26.

26. Burgner J TM, Vieira V, et al. System for robot assisted orthognathic surgery. Int J Comput Assist Radiol Surg 2007;2007:419-421.

27.

27. Burgner J ZY, Raczkowsky J, et al. Methods for end-effector coupling in robot assisted invervention. In: Robotics and automation, 2008 ICRA 2008 IEEE International Conference on Pasadena, CA, USA. 2008.

28.

28. Vieira VMM KG, Ionesco H, et al. Light-weight robot stability for orthognathic surgery. Phantom and animal cadavar trials.

29.

29. Han JJ, Woo SY, Yi WJ, Hwang SJ. Robot-Assisted Maxillary Positioning in Orthognathic Surgery: A Feasibility and Accuracy Evaluation. J Clin Med. 2021 Jun .

30.

30. Park JB "Development of repositioning method of maxillomandibular complex under movable head position using robot with 3D position recognizing function and navigation system in orthognathic surgery." 국내박사학위논문 서울대학교 대학원, 2019. 서울.

31.

31. Y. Lecun, L. Bottou, Y. Bengio and P. Haffner, "Gradientbased learning applied to document recognition," in Proceedings of the IEEE, vol. 86, no. 11, pp. 2278-2324, Nov. 1998, doi:10.1109/5.726791.

32.

32. He, K., Zhang, X., Ren, S. and Sun, J. Delving Deep into Rectifiers:Surpassing Human-Level Performance on ImageNet Classification. IEEE International Conference on Computer Vision. 2015;1502. 10.1109/ICCV.2015.123.

33.

33. Spampinato, C.; Palazzo, S.; Giordano, D.; Aldinucci, M.; Leonardi, R. Deep learning for automated skeletal bone age assessment in Xray images. Medical image analysis 2017, 36, 41-51, doi:10.1016/j. media.2016.10.010.

34.

34. Nogay, H.S.; Adeli, H. Detection of Epileptic Seizure Using Pretrained Deep Convolutional Neural Network and Transfer Learning. European neurology 2020, 83, 602-614, doi:10.1159/000512985.

35.

35. Men, K.; Chen, X.; Zhang, Y.; Zhang, T.; Dai, J.; Yi, J.; Li, Y. Deep Deconvolutional Neural Network for Target Segmentation of Nasopharyngeal Cancer in Planning Computed Tomography Images. Frontiers in Oncology 2017, 7, doi:10.3389/fonc.2017.00315.

36.

36. Lee, K.-S.; Jung, S.-K.; Ryu, J.-J.; Shin, S.-W.; Choi, J. Evaluation of Transfer Learning with Deep Convolutional Neural Networks for Screening Osteoporosis in Dental Panoramic Radiographs. Journal of clinical medicine 2020, 9, 392, doi:10.3390/jcm9020392.

37.

37. Neelapu, B.C.; Kharbanda, O.P.; Sardana, V.; Gupta, A.; Vasamsetti, S.; Balachandran, R.; Sardana, H.K. Automatic localization of three-dimensional cephalometric landmarks on CBCT images by extracting symmetry features of the skull. Dentomaxillofacial Radiology 2018, 47, 20170054, doi:10.1259/dmfr.20170054.

38.

38. Montúfar, J.; Romero, M.; Scougall-Vilchis, R.J. Hybrid approach for automatic cephalometric landmark annotation on cone-beam computed tomography volumes. American Journal of Orthodontics and Dentofacial Orthopedics 2018, 154, 140-150, doi:10.1016/j. ajodo.2017.08.028.

39.

39. Nishimoto, S.; Sotsuka, Y.; Kawai, K.; Ishise, H.; Kakibuchi, M. Personal Computer-Based Cephalometric Landmark Detection With Deep Learning, Using Cephalograms on the Internet. Journal of Craniofacial Surgery 2019, 30, 91-95, doi:10.1097/scs.0000000000004901.

40.

40. Baksi, S.; Freezer, S.; Matsumoto, T.; Dreyer, C. Accuracy of an automated method of 3D soft tissue landmark detection. European journal of orthodontics 2020, 10.1093/ejo/cjaa069, doi:10.1093/ejo/cjaa069.

41.

41. Grau, V.; Alcañiz, M.; Juan, M.C.; Monserrat, C.; Knoll, C. Automatic Localization of Cephalometric Landmarks. Journal of biomedical informatics 2001, 34, 146-156, doi:10.1006/jbin.2001.1014.

42.

42. Arık SÖ, Ibragimov B, Xing L. Fully automated quantitative cephalometry using convolutional neural networks. J Med Imaging (Bellingham). 2017;4(1):014501. doi:10.1117/1.JMI.4.1.014501.

43.

43. Wang CW, Huang CT, Hsieh MC, Li CH, Chang SW, Li WC, Vandaele R, Marée R, Jodogne S, Geurts P, Chen C, Zheng G, Chu C, Mirzaalian H, Hamarneh G, Vrtovec T, Ibragimov B. Evaluation and Comparison of Anatomical Landmark Detection Methods for Cephalometric X-Ray Images: A Grand Challenge. IEEE Trans Med Imaging. 2015 Sep;34(9):1890-900. doi: 10.1109/TMI.2015.2412951. Epub 2015 Mar 16. PMID: 25794388

44.

44. W ang CW, Huang CT, Lee JH, Li CH, Chang SW, Siao MJ, aLi TM, Ibragimov B, Vrtovec T, Ronneberger O, Fischer P, Cootes TF, Lindner C. A benchmark for comparison of dental radiography analysis algorithms. Med Image Anal. 2016 Jul;31:63-76. doi: 10.1016/j. media.2016.02.004. Epub 2016 Feb 28. PMID: 26974042.

45.

45. Shahidi Sh, Oshagh M, Gozin F, Salehi P, Danaei SM. Accuracy of computerized automatic identification of cephalometric landmarks by a designed software. Dentomaxillofac Radiol. 2013;42(1):20110187. doi: 10.1259/dmfr.20110187. Erratum in:Dentomaxillofac Radiol. 2013;42(4):20139010. Shahidi, S [corrected to Shahidi, Sh]. PMID: 23236215; PMCID: PMC3746488.

46.

46. Hwang HW, Park JH, Moon JH, et al. Automated identification of cephalometric landmarks: Part 2-Might it be better than human?. Angle Orthod. 2020;90(1):69-76. doi:10.2319/022019-129.1.

47.

47. Kim YH. Web based and Artificial Intelligence driven Orthodontic Analysis System. J Clin Digit Dent. 2019;1(2):24-28. www.jcdd.org

48.

48. Choi HI, Jung SK, Baek SH, Lim WH, Ahn SJ, Yang IH, Kim TW. Artificial Intelligent Model With Neural Network Machine Learning for the Diagnosis of Orthognathic Surgery. J Craniofac Surg. 2019Oct;30(7):1986-1989. doi: 10.1097/SCS.0000000000005650. Erratum in: J Craniofac Surg. 2020 Jun;31(4):1156. PMID: 31205280.

49.

49. Lee, K. S., Ryu, J. J., Jang, H. S., Lee, D. Y., & Jung, S. K. (2020). Deep convolutional neural networks based analysis of cephalometric radiographs for differential diagnosis of orthognathic surgery indications. Applied Sciences (Switzerland), 10(6), [2124]. https://doi.org/10.3390/app10062124.

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