Digital planning of orthodontic dental treatment: A literature review

Cover Page


Cite item

Full Text

Open Access Open Access
Restricted Access Access granted
Restricted Access Subscription or Fee Access

Abstract

This paper presents data on the incidence of malocclusion. It discusses the advantages of digital impressions over analog impressions in dental practice. The literature on software, digital planning methods, and the features of modern orthodontic treatment devices used in dentistry is reviewed. Comparative characteristics of each proposed method are provided. Furthermore, the potential applications of combination planning approaches, such as cone beam computed tomography and intraoral scanning, are discussed.

The review describes in detail the most common digital solutions used by dentists during orthodontic treatment planning, as well as their advantages and disadvantages.

Full Text

Restricted Access

About the authors

Samvel V. Apresyan

Peoples’ Friendship University of Russia

Email: dr.apresyan@gmail.com
ORCID iD: 0000-0002-3281-707X
SPIN-code: 6317-9002

MD, Dr. Sci. (Medicine), Associate Professor

Russian Federation, 6 Mikluho-Maklaja street, 117198 Moscow

Alexandr G. Stepanov

Peoples’ Friendship University of Russia

Author for correspondence.
Email: stepanovmd@list.ru
ORCID iD: 0000-0002-6543-0998
SPIN-code: 5848-6077

MD, Dr. Sci. (Medicine), Associate Professor

Russian Federation, 6 Mikluho-Maklaja street, 117198 Moscow

Oksana O. Moskovec

Peoples’ Friendship University of Russia

Email: om.stomat@gmail.com
ORCID iD: 0000-0002-6479-8192
SPIN-code: 2318-6028

MD, Cand. Sci. (Medicine), Associate Professor

Russian Federation, 6 Mikluho-Maklaja street, 117198 Moscow

Ellina A. Malieva

Peoples’ Friendship University of Russia

Email: malieva-elina@mail.ru
ORCID iD: 0009-0001-5586-1743
Russian Federation, 6 Mikluho-Maklaja street, 117198 Moscow

References

  1. Tsvetkova MA, Aksamit LA. Orthodontics and pathology of the oral mucosa. Moscow: MEDpress-inform; 2023. 96 p. (In Russ.) ISBN: 978-5-907632-84-4
  2. Persin LS. Orthodontics. National guidelines: Treatment of dental anomalies: in 2 volumes. Vol. 2. Moscow: GEOTAR-Media; 2020. 376 p. (In Russ.) doi: 10.33029/9704-5409-1-2-ONRD-2020-1-376
  3. Laganà G, Venza N, Borzabadi-Farahani A, et al. Dental anomalies: prevalence and associations between them in a large sample of non-orthodontic subjects, a cross-sectional study. BMC Oral Health. 2017;17(1):62. doi: 10.1186/s12903-017-0352-y
  4. Kuroedova VD, Makarova AN. The prevalence of dental anomalies in adults and the proportion of asymmetric forms among them. Svit medicini ta biologii. 2012;8(4):031–035. (In Russ.) EDN: PONXAT
  5. Lepilin AV, Dmitrienko SV, Domenyuk DA, et al. Dependence of stress strain of dental hard tissues and periodont on horizontal deformation degree. Archive Euromedica. 2019;9(1):173–194. EDN: VMYAFR doi: 10.35630/2199-885X/2019/9/1/173
  6. Yuzbasioglu E, Kurt H, Turunc R, Bilir H. Comparison of digital and conventional impression techniques: evaluation of patients’ perception, treatment comfort, effectiveness and clinical outcomes. BMC Oral Health. 2014;14:10. doi: 10.1186/1472-6831-14-10
  7. Mangano F, Gandolfi A, Luongo G, Logozzo S. Intraoral scanners in dentistry: a review of the current literature. BMC Oral Health. 2017;17(1):149. doi: 10.1186/s12903-017-0442-x
  8. Saccomanno S, Saran S, Vanella V, et al. The potential of digital impression in orthodontics. Dent J (Basel). 2022;10(8):147. doi: 10.3390/dj10080147
  9. Pahuja N, Doneria D, Mathur S. Comparative evaluation of accuracy of intraoral scanners vs conventional method in establishing dental measurements in mixed dentition. World J Dent. 2023;14 (5):419–424. doi: 10.5005/jp-journals-10015-2231
  10. Arsenina OI, Komarova AV, Popova NV. Digital technologies for treatment of class ii patients with musculo-articular dysfunction. Ortodontija. 2022;99(3):28–33. EDN: GQFKPP
  11. Ivanov SYu, Dmitrienko SV, Domenyuk DA, et al. Variability of morphometric parameters of dental arches and bone structures of the temporomandibular joint in physiological variants of occlusive relationships. The Dental Institute. 2021;(3):44–47. EDN: JWFDUL
  12. Hadadpour S, Noruzian M, Abdi AH, et al. Can 3D imaging and digital software increase the ability to predict dental arch form after orthodontic treatment? Am J Orthod Dentofacial Orthop. 2019;156(6):870–877. doi: 10.1016/j.ajodo.2019.07.009
  13. Ermakov AV, Losev AV. Intraoral scanners in orthodontics review. Russian Journal of Stomatology. 2023;16(3):44–48. EDN: XNCKJQ doi: 10.17116/rosstomat20231603144
  14. Lee KM. Comparison of two intraoral scanners based on three-dimensional surface analysis. Prog Orthod. 2018;19(1):6. doi: 10.1186/s40510-018-0205-5
  15. Schlenz MA, Schupp B, Schmidt A, et al. New caries diagnostic tools in intraoral scanners: a comparative in vitro study to established methods in permanent and primary teeth. Sensors (Basel). 2022;22(6):2156. doi: 10.3390/s22062156
  16. Deferm JT, Schreurs R, Baan F, et al. Validation of 3D documentation of palatal soft tissue shape, color, and irregularity with intraoral scanning. Clin Oral Investig. 2018;22(3):1303–1309. doi: 10.1007/s00784-017-2198-8
  17. Borodina ID, Grigoryants LS, Gadzhiev MA, et al. Comparative evaluation of the accuracy of the dental arch display using modern intraoral three-dimensional scanners. Russian Journal of Dentistry. 2022;26(4):287–297. EDN: NPAGCH doi: 10.17816/1728-2802-2022-26-4-287-297
  18. Rybakov A. Optimization of orthodontic treatment based on neural networks, finite element analysis and digital maps of the oral mucosa [dissertation]. Saint Petersburg; 2024. Available from: https://disser.spbu.ru/files/2024/disser_rybakov_aleksandr.pdf (In Russ.) EDN: WCXDWJ
  19. Rozov RA, Trezubov VN, Shalaginova AV, Koussevitsky LYa. Comparative in vitro evaluation of the accuracy of dental open system scanners. Parodontologiya. 2020;25(3):231–236. EDN: MMDCTO doi: 10.33925/1683-3759-2020-25-3-231-236
  20. Natsubori R, Fukazawa S, Chiba T, et al. In vitro comparative analysis of scanning accuracy of intraoral and laboratory scanners in measuring the distance between multiple implants. Int J Implant Dent. 2022;8(1):18. doi: 10.1186/s40729-022-00416-4
  21. Park GH, Son K, Lee KB. Feasibility of using an intraoral scanner for a complete-arch digital scan. J Prosthet Dent. 2019;121(5):803–810. doi: 10.1016/j.prosdent.2018.07.014
  22. Apresyan SV. Integrated digital planning of dental treatment [dissertation]. Moscow; 2020. Available from: https://www.dissercat.com/content/kompleksnoe-tsifrovoe-planirovanie-stomatologicheskogo-lecheniya (In Russ.) EDN: LWZSAG
  23. Lin H, Pan Y, Wei X, et al. Comparison of the performance of various virtual articulator mounting procedures: a self-controlled clinical study. Clin Oral Investig. 2023;27(7):4017–4028. doi: 10.1007/s00784-023-05028-9
  24. Thurzo A, Strunga M, Havlínová R, et al. Smartphone-based facial scanning as a viable tool for facially driven orthodontics? Sensors (Basel). 2022;22(20):7752. doi: 10.3390/s22207752
  25. Apresyan SV, Stepanov AG, Antonik MM, et al. Complex digital planning of stomatological treatment (practical guide). Apresyan SV, editor. Moscow: Mozartika; 2020. (In Russ). EDN: BFHWAT
  26. De Vos W, Casselman J, Swennen GR. Cone-beam computerized tomography (CBCT) imaging of the oral and maxillofacial region: a systematic review of the literature. Int J Oral Maxillofac Surg. 2009;38(6):609–625. doi: 10.1016/j.ijom.2009.02.028
  27. Khvostenko EA. Orthodontic treatment of patients with dentition anomalies using fixed devices and orthodontic screws [dissertation abstract]. Moscow; 2023. (In Russ.) EDN: VFHWAF
  28. Makhortova PI. Clinical and radiological comparison of methods of combined treatment of patients with narrowing of the upper jaw [dissertation]. Moscow; 2020. Available from: https://www.dissercat.com/content/kliniko-rentgenologicheskoe-sravnenie-metodov-kombinirovannogo-lecheniya-patsientov-s-suzhen (In Russ). EDN: TQEVDI
  29. Vasiliev Yu. A. Digital microfocus technology of radiography in the assessment of the anatomical structure of teeth: an experimental study [dissertation]. Saint Petersburg; 2015. Available from: https://viewer.rsl.ru/ru/rsl01005565409?page=1&rotate=0&theme=white (In Russ). EDN: NEBUBX
  30. Staroverov NE, Gryaznov AYu, Potrakhov NN, et al. New methods of digital processing of microfocus X-ray images. Medicinskaja tehnika. 2018;6(312):53–55. (In Russ.) EDN: YWIGMX
  31. Nichipore EA. The possibilities of microfocus cone-beam computed tomography in the visualization of dental materials and foreign objects: an experimental study [dissertation]. Moscow, 2021. Available from: https://dissov.msmsu-portal.ru/image/image/2023/06/02/Диссертация_Ничипор_ЕА.pdf (In Russ.) EDN: VUIOLH
  32. Pirilä-Parkkinen K, Löppönen H, Nieminen P, et al. Validity of upper airway assessment in children: a clinical, cephalometric, and MRI study. Angle Orthod. 2011;81(3):433–439. doi: 10.2319/063010-362.1
  33. Apresyan SV, Stepanov AG, Sopotsinsky DV, et al. 3D planning of dental treatment. Methodical manual. Moscow: Novik; 2020. 140 p. (In Russ.)
  34. Abesi F, Maleki M, Zamani M. Diagnostic performance of artificial intelligence using cone-beam computed tomography imaging of the oral and maxillofacial region: A scoping review and meta-analysis. Imaging Sci Dent. 2023;53(2):101–108. doi: 10.5624/isd.20220224
  35. Verhelst PJ, Smolders A, Beznik T, et al. Layered deep learning for automatic mandibular segmentation in cone-beam computed tomography. J Dent. 2021;114:103786. doi: 10.1016/j.jdent.2021.103786
  36. Ahmed N, Abbasi MS, Zuberi F, et al. Artificial intelligence techniques: analysis, application, and outcome in dentistry — a systematic review. Biomed Res Int. 2021;2021:9751564. doi: 10.1155/2021/9751564
  37. Pethani F. Promises and perils of artificial intelligence in dentistry. Aust Dent J. 2021;66(2):124–135. doi: 10.1111/adj.12812
  38. Kordass B, Gärtner C, Söhnel A, et al. The virtual articulator in dentistry: concept and development. Dent Clin North Am. 2002;46(3):493–506. doi: 10.1016/s0011-8532(02)00006-X
  39. Carossa M, Cavagnetto D, Ceruti P, et al. Individual mandibular movement registration and reproduction using an optoeletronic jaw movement analyzer and a dedicated robot: a dental technique. BMC Oral Health. 2020;20(1):271. doi: 10.1186/s12903-020-01257-6
  40. Revilla-León M, Kois DE, Kois JC. A guide for maximizing the accuracy of intraoral digital scans. Part 1: Operator factors. J Esthet Restor Dent. 2023;35(1):230–240. doi: 10.1111/jerd.12985
  41. Solaberrieta E, Garmendia A, Minguez R, et al. Virtual facebow technique. J Prosthet Dent. 2015;114(6):751–755. doi: 10.1016/j.prosdent.2015.06.012
  42. Panteleev VD, Roshchin EM, Panteleev SV. Diagnostics of mandibular articulation disorders in tmj dysfunction patients. Stomatology. 2011;90(1):52–57. (In Russ.) EDN: OYEMWN
  43. Parhamovich SN, Bitno VL, Bitno MV. Comparative analysis of modern methods for registration of the hinge axis. Modern dentistry. 2020;(1):80–85. EDN: STLWWP
  44. Grigorenko MP. Digital approaches to diagnosis and treatment of patients with dental arch shape abnormalities [dissertation]. Stavropol; 2024. Available from: https://rusneb.ru/catalog/000199_000009_012687348/ (In Russ.) EDN: CEGQSF
  45. Arutyunov SD, Gvetadze RSh, Lebedenko IYu, Stepanov AG. Innovative solutions in dentistry. Moscow: Practical Medicine; 2019. (In Russ.) ISBN: 978-5-98811-569-4 EDN: BRABVP ISBN: 978-5-98811-569-4
  46. Castroflorio T, Sedran A, Parrini S, et al. Predictability of orthodontic tooth movement with aligners: effect of treatment design. Prog Orthod. 2023;24(1):2. doi: 10.1186/s40510-022-00453-0
  47. Tsolakis IA, Panos P, Papadopoulos MA. Accuracy of the ClinCheck prediction outcome for orthodontic treatment with Invisalign. A 3D digital casts study. Conference: International Symposium, Greek Orthodontic Society. 2022.
  48. Pilipenko ND, Maksyukov SYu. Accuracy of predicting the upper arch expansion using the ClinCheck software. Russian Journal of Dentistry. 2021:25(2):159–166. EDN: KKYDWU doi: 10.17816/1728-2802-2021-25-2-159-166
  49. Ryakhovsky AN, Boitsova EA. 3D analysis of the temporomandibular joint and occlusal relationships based on computer virtual simulation. Stomatology.2020;99(2):97–104. EDN: SYSPXL doi: 10.17116/stomat20209902197
  50. Liang YM, Rutchakitprakarn L, Kuang SH, Wu TY. Comparing the reliability and accuracy of clinical measurements using plaster model and the digital model system based on crowding severity. J Chin Med Assoc. 2018;81(9):842–847. doi: 10.1016/j.jcma.2017.11.011
  51. Eid HSE, Elhiny OA. Evaluation of an open access generic 3D software for Orthodontic diagnosis and treatment planning. Brazilian Journal of Oral Sciences. 2021;21:e227903. doi: 10.20396/bjos.v21i00.8667903
  52. Hammam KI, Nassef E, AL Dawltaly M. Comparing program compatibility for dental operators between different in-office clear aligners software. Future Dental Journal. 2024;9(2):111–115. doi: 10.54623/fdj.9027

Supplementary files

Supplementary Files
Action
1. JATS XML

Copyright (c) 2024 Eco-Vector

License URL: https://eco-vector.com/for_authors.php#07

СМИ зарегистрировано Федеральной службой по надзору в сфере связи, информационных технологий и массовых коммуникаций (Роскомнадзор).
Регистрационный номер и дата принятия решения о регистрации СМИ: серия ПИ № ФС 77 - 86295 от 11.12.2023 г
СМИ зарегистрировано Федеральной службой по надзору в сфере связи, информационных технологий и массовых коммуникаций (Роскомнадзор).
Регистрационный номер и дата принятия решения о регистрации СМИ: серия ЭЛ № ФС 77 - 80635 от 15.03.2021 г
.