employee
Tver', Tver, Russian Federation
employee
Tver, Tver, Russian Federation
employee
Tver', Tver, Russian Federation
Tver, Tver, Russian Federation
Tver, Tver, Russian Federation
UDK 616 Патология. Клиническая медицина
The aim of study. Study the available specialized literature on the use of CBCT in maxillofacial imaging and the combination of this research method with artificial intelligence to improve the diagnosis and treatment of complex dental diseases. Methodology. The data of special literature were studied using scientific search library databases: Pub Med, Elibrary, Cochrane, Google Scholar. Results. The use of cone-beam computed tomography (CBCT) in the examination of patients in need of prosthetics allows to obtain a large amount of diagnostic information about the condition of hard tissues of teeth, bone tissue of jaws, temporomandibular joint and nearby anatomical structures in comparison with other radiological methods of additional examination of patients. The possibilities of this type of research were evaluated together with a representative of the "Diagnocat" artificial intelligence system and an analysis of its advantages was carried out. It also describes a comprehensive protocol for planning orthopedic treatment of patients based on digital (virtual) modeling and its advantages for a practicing orthopedic dentist. Conclusions. The CBCT method is widely used in modern dental practice due to its accuracy, accessibility and high objectivity. Artificial intelligence technologies introduced into the planning process of complex dental treatment are gradually becoming a tool for the practitioner. Automatic recognition of teeth and diagnostics of facial deformities using artificial intelligence systems based on CBCT are very likely to become an area of increased interest in the future. The review is aimed at giving practicing dentists and interested colleagues in the field of healthcare a comprehensive understanding of the current trend in the development of artificial intelligence in the field of 3D visualization in dental medicine.
cone-beam computed tomography, indications for dental depulpation, radiation risk in dentistry, artificial intelligence in dentistry, protocol of orthopedic treatment
1. He J., Baxter S.L., Xu J., Xu J., Zhou X., Zhang K. The practical implementation of artificial intelligence technologies in medicine // Journal of the Nat Med. - 2019;25(1):30-36. DOI:https://doi.org/10.1038/s41591-018-0307-0
2. Hosny A., Parmar C., Quackenbush J., Schwartz L.H., Aerts H.J.W.L. Artificial intelligence in radiology // Journal of the Nat Rev Cancer. - 2018;18(8):500-510. DOI:https://doi.org/10.1038/s41568-018-0016-5
3. Fazal M.I., Patel M.E., Tye J., Gupta Y. The past, present and future role of artificial intelligence in imaging // Journal of the European Radiology. - 2018;105:246-250. https://doi.org/10.1016/j.ejrad.2018.06.020
4. Chen Y.W., Stanley K., Att W. Artificial intelligence in dentistry: Current applications and future perspectives // Quintessence Int. - 2020;51:248-257. DOI:https://doi.org/10.3290/j.qi.a43952
5. Kim T., Cho Y., Kim D., Chang M., Kim Y.J. Tooth segmentation of 3D scan data using generative adversarial networks // Journal Applied Sciences. - 2020;10:490. https://doi.org/10.3390/app10020490
6. Chan M., Dadul T., Langlais R., Russell D., Ahmad M. Accuracy of extraoral bite-wing radiography in detecting proximal caries and crestal bone loss. // Journal of the American Dental Association. - 2018;149(1):51-58. https://doi.org/10.1016/j.adaj.2017.08.032
7. Vokulova Yu.A. Razrabotka i vnedrenie cifrovyh tehnologiĭ pri ortopedicheskom lechenii s primeneniem nes'emnyh protezov zubov : dis. ... k.m.n. Nizhniy Novgorod, 2017:22. [Yu.A. Vokulova. Development and implementation of digital technologies in orthopedic treatment with the use of fixed dentures : master’s thesis. Nizhniy Novgorod, 2017:22. (In Russ.)]. https://www.elibrary.ru/item.asp?id=30440885
8. Yoon D.C., Mol A., Benn D.K., Benavides E. Digital radiographic image processing and analysis // Journal of Dental Clinics of North America. - 2018;62:341-359. https://doi.org/10.1016/j.cden.2018.03.001
9. Jain S., Choudhary K., Nagi R., Shukla S., Kaur N., Grover D. New evolution of cone-beam computed tomography in dentistry: Combining digital technologies // Journal of Imaging Science Dentistry. - 2019;49:179-190. https://doi.org/10.5624/isd.2019.49.3.179
10. Hayashi T., Arai Y., Chikui T., Hayashi-Sakai S., Honda K., Indo H. et al. Clinical guidelines for dental cone-beam computed tomography // Journal of Oral Radiology. - 2018;34:89-104. https://doi.org/10.1007/s11282-018-0314-3
11. Beam A.L., Kohane I.S. Big Data and Machine Learning in Health Care // Journal of the American Medical Association. - 2018;319(13):1317-1318. DOI:https://doi.org/10.1001/jama.2017.18391
12. Stiller W. Basics of iterative reconstruction methods in computed tomography: a vendor-independent overview // European Journal of Radiology. - 2018;109:147-154. https://doi.org/10.1016/j.ejrad.2018.10.025
13. Bayrakdar I.S. et al. Cone beam computed tomography and ultrasonography imaging of benign intraosseous jaw lesion: A prospective radiopathological study // Journal of Clinical Oral Investigations. - 2018;22(3):1531-1539. https://doi.org.1007/s00784-017-2257-1
14. Orhan K., Bayrakdar I.S., Ezhov M., Kravtsov A., Ozyurek T. Evaluation of artificial intelligence for detecting periapical pathosis on cone-beam computed tomography scans // Journal of International Endodontic. - 2020;53(5):680-689. https://doi.org/10.1111/iej.13265
15. Estrela C., Bueno M.R., Leles C.R., Azevedo B., Azevedo J.R. Accuracy of cone beam computed tomography and panoramic and periapical radiography for detection of apical periodontitis // Journal of Endododontics. - 2018;34(3):273-279. https://doi.org/10.1016/j.joen.2007.11.023
16. Niebler S., Schömer E., Tjaden H., Schwanecke U., Schulze R. Projection-based improvement of 3D reconstructions from motion-impaired dental cone beam CT data // Med Phys. - 2019;46:4470-4480. https://doi.org/10.1002/mp.v46.1https://doi.org/10.1002/mp.13731
17. Kalra M.K. Artificial intelligence in image reconstruction: The change is here // Journal of Medical Physics. - 2020;79:113-125. https://doi.org/10.1016/j.ejmp.2020.11.012
18. Ramis-Alario A. et al. Comparison of diagnostic accuracy between periapical and panoramic radiographs and cone beam computed tomography in measuring the periapical area of teeth scheduled for periapical surgery. A cross-sectional study // Journal of Clinical and Experimental Dentistry. - 2019;11(8):732-738. https://doi.org/10.4317/jced.55986
19. Sheth N.M., Zbijewski W., Jacobson M.W., Abiola G., Kleinszig G., Vogt S. et al. Mobile C-Arm with a CMOS detector: Technical assessment of fluoroscopy and Cone-Beam CT imaging performance // Journal of Medical Physics. -2018;45:5420-5436. https://doi.org/10.1002/mp.13244
20. Santaella G.M., Wenzel A., Haiter-Neto F., Rosalen P.L., Spin-Neto R. Impact of movement and motion-artefact correction on image quality and interpretability in CBCT units with aligned and lateral-offset detectors // Journal of Dentomaxillofacial Radiology. - 2020;49:3-10. https://doi.org/10.1259/dmfr.20190240
21. Mutalik S., Tadinada A., Molina M.R., Sinisterra A., Lurie A. Effective doses of dental cone beam computed tomography: effect of 360-degree versus 180-degree rotation angles // Journal of Oral Surgery. - 2020;130:433-446. https://doi.org/10.1016/j.oooo.2020.04.008
22. Yeung A.W.K., Jacobs R., Bornstein M.M. Novel low-dose protocols using cone beam computed tomography in dental medicine: a review focusing on indications, limitations, and future possibilities // Journal Clinical Oral Investigations. - 2019;23:2573-2581. https://doi.org/10.1007/s00784-019-02907-y
23. Siiskonen T., Gallagher A., Ciraj Bjelac O., Novak L., Sans Merce M., Farah J. et al. A European perspective on Dental Cone Beam Computed Tomography (CBCT) systems with a focus on optimisation utilising DRLs (Diagnostic Reference Levels) // Journal Radiological Protection. - 2021;41(2):3-5. https://doi.org/10.1088/1361-6498/abdd05
24. Mah E., Ritenour E.R., Yao H. A review of dental cone-beam CT dose conversion coefficients // Journal Dentomaxillofacial Radiology. - 2021;50:3-8. https://doi.org/10.1259/dmfr.20200225
25. Weiss 2nd, R., Read-Fuller A. Cone Beam Computed Tomography in Oral and Maxillofacial Surgery: An Evidence-Based Review // Journal Dentistry journal (Basel). - 2019;7:52. https://doi.org/10.3390/dj7020052
26. Beganović A., Ciraj-Bjelac O., Dyakov I., Gershan V., Kralik I. Milatović A. et al. IAEA survey of dental cone beam computed tomography practice and related patient exposure in nine Central and Eastern European countries // Dentomaxillofacial Radiology. - 2020;49:3-12. https://doi.org/10.1259/dmfr.20190157
27. Deleu M., Dagassan D., Berg I., Bize J., Dula K., Lenoir V. et al. Establishment of national diagnostic reference levels in dental cone beam computed tomography in Switzerland // Journal Dentomaxillofacial Radiology. - 2020;49:2-6. https://doi.org/10.1259/dmfr.20190468
28. Reddy R.S. et al. Knowledge and attitude of dental fraternity towards cone beam computed tomography in south India - A questionnaire study // Indian Journal of Dental Reseach - 2012;4:88-94. https://doi.org/10.1016/j.ijd.2012.10.003
29. Hung K., Montalvao C., Tanaka R., Kawai T., Bornstein M.M. The use and performance of artificial intelligence applications in dental and maxillofacial radiology: A systematic review // Journal Dentomaxillofacial Radiology. - 2020;49(1):5-10. https://doi.org/10.1259/dmfr.20190107
30. American Dental Association Council on Scientific Affairs. The use of cone-beam computed tomography in dentistry: an advisory statement from the American Dental Association Council on Scientific Affairs // Journal of the American Dental Association. - 2012;143:899-092. https://doi.org/10.14219/jada.archive.2012.0295
31. Hosny A., Parmar C., Quackenbush J., Schwartz L.H., Aerts H. J. Artificial intelligence in radiology // Journal of Nature Reviews Cancer. - 2018;18(8):500-510. https://doi.org/10.1038/s41568-018-0016-5
32. Chen H. et al. A deep learning approach to automatic teeth detection and numbering based on object detection in dental periapical films // Journal of Scientific Reports. - 2019;9(1):1-11. https://doi.org/10.1038/s41598-019-40414-y
33. Kim I.H., Singer S.R., Mupparapu M. Review of cone beam computed tomography guidelines in North America // Quintessence International Publishing Group. - 2019;50:136-145. https://doi.org/10.3290/j.qi.a41332
34. Oenning A.C. et al. Cone-beam CT in paediatric dentistry: DIMITRA project position statement // Journal of Pediatric Radiology. - 2018;48(3):308-316. https://doi.org/10.1007/s00247-017-4012-9
35. Horner K. et al. Diagnostic efficacy of cone beam computed tomography in paediatric dentistry: A systematic review // Journal of European Archives of Paediatric Dentistry. - 2020;21(4):407-426. https://doi.org/10.1007/s40368-019-00504-x
36. Ekert T. et al. Deep learning for the radiographic detection of apical lesions // Journal of Endodontics. - 2019;45(7):917-922. https://doi.org/10.1016/j.joen.2019.03.016
37. Fukuda M. et al. Evaluation of an artificial intelligence system for detecting vertical root fracture on panoramic radiography // Journal of Oral Radiology. - 2019;36(4):337-343. https://doi.org/10.1007/s11282-019-00409-x
38. Krois J. et al. Deep learning for the radiographic detection of periodontal bone loss // Journal of Scientific Reports. - 2019;9(1):8495. https://doi.org/10.1038/s41598-019-44839-3
39. Lee J.H., Kim D.H., Jeong S.N. Diagnosis of cystic lesions using panoramic and cone beam computed tomographic images based on deep learning neural network // Journal of Oral Diseases. - 2020;26(1):152-158. https://doi.org/10.1111/odi.13223
40. Lee J.H., Kim D.H., Jeong S.N., Choi S.H. Detection and diagnosis of dental caries using a deep learning-based convolutional neural network algorithm // Journal of Dental Research. - 2018;77:106-111. https://doi.org/10.1016/j.jdent.2018.07.015
41. Merdietio Boedi R. et al. Effect of lower third molar segmentations on automated tooth development staging using a convolutional neural network // Journal of Forensic Sciences. - 2020;65(2):481-486. https://doi.org/10.1111/1556-4029.14182
42. Matzen L.H., Berkhout E. Cone beam CT imaging of the mandibular third molar: a position paper prepared by the European Academy of Dentomaxillofacial Radiology (EADMFR) // Journal of Dentomaxillofacial Radiology. - 2019;48:2-5. https://doi.org/10.1259/dmfr.20190039
43. Hayashi T., Arai Y., Chikui T., Hayashi-Sakai S., Honda K., Indo H. et al. Clinical guidelines for dental cone-beam computed tomography // Journal Oral Radiology. - 2018;34:89-104. https://doi.org/10.1007/s11282-018-0314-3
44. Poedjiastoeti W., Suebnukarn S. Application of convolutional neural network in the diagnosis of jaw tumors // Journal of Healthcare Informatics Research - 2018;24(3):236-241. https://doi.org/10.4258/hir.2018.24.3.236
45. Schwendicke F., Golla T., Dreher M., Krois J. Convolutional neural networks for dental image diagnostics: A scoping review // Journal Dentistry. - 2019;91:2-3. https://doi.org/10.1016/j.jdent.2019.103226.
46. Ezhov M., Gusarev M., Golitsyna M., Julian M. Yates, Kushnerev E., Tamimi D., Secil Aksoy, Shumilov E., Alex Sanders, Kaan Orhan. Clinically applicable artificial intelligence system for dental diagnosis with CBCT // Scientific Reports. - 2022;11(1):2-15. https://doi.org/10.1038/s41598-021-94093-9.
47. Nasseh I., Al-Rawi W. Cone beam computed tomography // Journal Dental Clinics North America. - 2018;62:361-391. https://doi.org/10.1016/j.cden.2018.03.002
48. Batyukov N.M., Prohvatilov O.G., Chibisova M.A. Primenenie konusno-luchevoy komp'yuternoy tomografii na etapah ortopedicheskogo lecheniya. Institut Stomatologii. 2020;1(86):34-36. [N.M. Batyukov, O.G. Prokhvatilov, M.A. Chibisova. The use of cone-beam computed tomography at the stages of orthopedic treatment. Institute of Dentistry. 2020;1(86):34-36. (In Russ.)]. https://elibrary.ru/item.asp?id=43932821
49. Sarment D., Berning J.A., Snyder C.J., Hetzel S. Analysis of the Anatomic Relationship Between the Mandibular First Molar Roots and Mandibular Canal Using Cone-Beam Computed-Tomography in 101 Dogs // Frontiers in Veterinary Science. - 2020;6:485-488. https://doi.org/10.3389/fvets.2019.00485
50. Weiss 2nd, R., Read-Fuller A. Cone Beam Computed Tomography in Oral and Maxillofacial Surgery: An Evidence-Based Review // Dentistry Journal (Basel). - 2019;7:52. https://doi.org/10.3390/dj7020052
51. Ranjan T., Gangaiah M., Chaubey A.K., Wadhwa I., Nischal K. Implant and prosthetic planning using cone beam computed tomography and radiographic markers for full mouth-fixed implant- supported prosthesis // Journal of Dental Implants. - 2018;8(1):37-39. DOIhttps://doi.org/10.4103/jdi.jdi_5_18
52. Koshelev K.A., Belousov N.N., Baranov I.P., Nikonorov V.I. Izuchenie vstrechaemosti oslozhneniy stomatologicheskogo ortopedicheskogo lecheniya u pacientov s saharnym diabetom. Problemy stomatologii. 2020;2(16):101-107. [K.A. Koshelev, N.N. Belousov, I.P. Baranov, V.I. Nikonorov. Study of the occurrence of complications of dental orthopedic treatment in patients with diabetes mellitus. Actual problems in dentistry. 2020;2(16):101-107. (In Russ.)]. https://elibrary.ru/item.asp?id=43783714
53. Koshelev K.A., Belousov N.N., Sokolova I.V., Sokolov D.O. Prognozirovanie srokov pol'zovaniya razlichnyh vidov zubnyh protezov u pacientov s gipertonicheskoy bolezn'yu. Problemy stomatologii. 2020;1(16):143-148. [K.A. Koshelev, N.N. Belousov, I.V. Sokolova, D.O. Sokolov. Forecasting the terms of use of various types of dentures in patients with hypertension. Actual problems in dentistry. 2020;1(16):143-148. (In Russ.)]. https://elibrary.ru/item.asp?id=42817264
