Moscow, Moscow, Russian Federation
Moscow, Moscow, Russian Federation
Moscow, Moscow, Russian Federation
Moscow, Moscow, Russian Federation
GRNTI 76.29 Клиническая медицина
OKSO 31.05.03 Стоматология
BBK 566 Стоматология
TBK 573 Клиническая медицина в целом
Over the past few years, artificial intelligence (AI) technologies have been actively used in many areas of medicine, including dentistry. The aim of the study is to determine the diagnostic value of IS in the detection of caries and its complications according to cone beam computed tomography (CBCT) data in comparison with clinical examination. Materials and methods. CBCT images of 15 patients with carious and periodontal lesions were analyzed by an experienced dentist, who also specializes in radiology, and the Diagnocat AI software. The dentist also performed a visual examination of these patients. Results. Most of all contact caries were determined using AI (n = 20), and occlusal caries − during clinical examination (n = 10). The greatest number of periapical changes was also detected using IS (n = 22). The difference between the indicators of detection of pathological foci in the assessment of IS and the radiologist was statistically insignificant, which indicates the equivalence of these methods. X-ray image evaluation revealed more contact caries compared to clinical examination (14 vs. 7, p < 0.05), but clinical examination was superior in detecting occlusal caries (10 vs. 2, p < 0.03). Periodontal disease was more accurately diagnosed by X-ray (17 vs. 9, p < 0.05). The average time for evaluation of CBCT images by a radiologist was 21.54 ± 4.4 minutes, and the AI completed the report in 4.6 ± 4.4 minutes from the moment the loading of CBCT was completed (p < 0.01). Conclusion. The use of AI technologies in the analysis of CBCT images can improve the accuracy of diagnosing caries and its complications by up to 98%, as well as significantly speed up the time for making a diagnostic decision.
artificial intelligence, caries, diagnostics, cone beam computed tomography, periapical changes
1. Burda A.N., Rutkovskaya A.S. Diagnostika skrytogo kariesa s pomosch'yu rentgen-diagnostiki BITEWING. Sovremennaya stomatologiya. 2020;3:86-90. [A.N. Burda, A.S. Rutkovskaya. Diagnosis of latent caries using BITEWING X-ray diagnostics. Modern dentistry. 2020;3:86-90. (In Russ.)]. https://www.elibrary.ru/item.asp?id=44144549
2. Kazumyan S.V., Degtev I.A., Borisov V.V., Ershov K.A. Virtual'nye tehnologii v stomatologii. Vestnik Avicenny. 2020;22(4):606-612. [S.V. Kazumyan, I.A. Degtev, V.V. Borisov, K.A. Ershov. Virtual technologies in dentistry. Bulletin of Avicenna. 2020;22(4):606-612. (In Russ.)]. doi:https://doi.org/10.25005/2074-0581-2020-22-4-606-612
3. Pal'mov S.V., Bahmurina A.A. Ispol'zovanie neyronnyh setey v stomatologii. Problemy razvitiya predpriyatiy: teoriya i praktika. 2020;1-2:237-240. [S.V. Palmov, A.A. Bakhmurina. The use of neural networks in dentistry. Problems of enterprise development: theory and practice. 2020;1-2:237-240. (In Russ.)]. https://www.elibrary.ru/item.asp?id=44800679
4. Abdalla-Aslan R., Yeshua T., Kabla D., Nadler C. An artificial intelligence system using machine-learning for automatic detection and classification of dental restorations in panoramic radiography // Oral Surg Oral Med Oral Pathol Oral Radiol. - 2020;130(5):593-602. https://doi.org/10.1016/j.oooo.2020.05.012
5. Anwar S.M., Majid M., Qayyum A., Awais M., Alnowami M., Khan K. Medical Image Analysis using Convolutional Neural Networks: A Review // J Med Syst. - 2018;42;11:226. https://doi.org/10.1007/s10916-018-1088-1
6. Balyen L., Peto T. Promising Artificial Intelligence-Machine Learning-Deep Learning Algorithms in Ophthalmology // Asia Pac J Ophthalmol (Phila). - 2019;8(3):264-272. doi:https://doi.org/10.22608/APO.2018479
7. Bayrakdar S.K., Orhan K., Bayrakdar I.S., Bilgir E., Ezhov M., Gusarev M., Shumilov E. A deep learning approach for dental implant planning in cone-beam computed tomography images // BMC Med Imaging. - 2021;21(1):86. https://doi.org/10.1186/s12880-021-00618-z
8. Casalegno F., Newton T., Daher R., Abdelaziz M., Lodi-Rizzini A., Schürmann F., Krejci I., Markram H. Caries Detection with Near-Infrared Transillumination Using Deep Learning // J Dent Res. - 2019;98;11:1227-1233. https://doi.org/10.1177/0022034519871884
9. Chen Y.-W., Stanley K., Att W. Artificial intelligence in dentistry: current applications and future perspectives // Quintessence Int. - 2020;1(3):248-257. DOI:https://doi.org/10.3290/j.qi.a44465
10. Choi H.I., Jung S.-K., Baek S.-H., Lim W.H., Ahn S.-J., Yang I.-H., Kim T.-W. Artificial Intelligent Model With Neural Network Machine Learning for the Diagnosis of Orthognathic Surgery // J Craniofac Surg. - 2019;30;7:1986-1989. doi:https://doi.org/10.1097/SCS.0000000000005650
11. Devito K.L., de Souza Barbosa F., Felippe Filho W.N. An artificial multilayer perceptron neural network for diagnosis of proximal dental caries // Oral Surgery, Oral Medicine, Oral Pathology, Oral Radiology, and Endodontology. - 2008;106(6):879-884. https://doi.org/10.1016/j.tripleo.2008.03.002
12. Esteva A., Kuprel B., Novoa R.A., Ko J., Swetter S.M., Blau H.M., Thrun S. Dermatologist-level classification of skin cancer with deep neural networks // Nature. - 2017;542;7639:115-118. https://doi.org/10.1038/nature21056
13. Estrela C., Bueno M.R., De Alencar A.H.G., Mattar R., Neto J.V., Azevedo B.C., De Araújo Estrela C.R. Method to evaluate inflammatory root resorption by using cone beam computed tomography // J Endod. - 2009;35;11:1491-1497. https://doi.org/10.1016/j.joen.2009.08.009
14. Ezhov M., Gusarev M., Golitsyna M., Yates J.M., Kushnerev E., Tamimi D., Aksoy S., Shumilov E., Sanders A., Orhan K. Clinically applicable artificial intelligence system for dental diagnosis with CBCT // Scientific reports. - 2021;11(1):1-16. https://doi.org/10.1038/s41598-021-94093-9
15. Fazal M.I., Patel M.E., Tye J., Gupta Y. The past, present and future role of artificial intelligence in imaging // Eur J Radiol. - 2018;5:246-250. https://doi.org/10.1016/j.ejrad.2018.06.020
16. Ferizi U., Besser H., Hysi P., Jacobs J., Rajapakse C.S., Chen C., Saha P.K., Honig S., Chang G. Artificial Intelligence Applied to Osteoporosis: A Performance Comparison of Machine Learning Algorithms in Predicting Fragility Fractures From MRI Data // J Magn Reson Imaging. - 2019;49(4):1029-1038. https://doi.org/10.1002/jmri.26280
17. Geetha V., Aprameya K.S., Hinduja D.M. Dental caries diagnosis in digital radiographs using back-propagation neural network // Health Information Science and Systems. - 2020;8(1):1-14. https://doi.org/10.1007/s13755-019-0096-y
18. Girard M.J.A., Schmetterer L. Artificial intelligence and deep learning in glaucoma: Current state and future prospects // Prog Brain Res. - 2020;257:37-64. https://doi.org/10.1016/bs.pbr.2020.07.002
19. Grischke J., Johannsmeier L., Eich L., Griga L., Haddadin S. Dentronics: Towards robotics and artificial intelligence in dentistry // Dent Mater. - 2020;36(6):765-778. https://doi.org/10.1016/j.dental.2020.03.021
20. Hung K., Yeung A.W.K., Tanaka R., Bornstein M.M. Current Applications, Opportunities, and Limitations of AI for 3D Imaging in Dental Research and Practice // Int J Environ Res Public Health. - 2020;17(12):4424. https://doi.org/10.3390/ijerph17124424
21. Javed S., Zakirulla M., Baig R.U., Asif S.M., Meer A.B. Development of artificial neural network model for prediction of post-streptococcus mutans in dental caries // Comput Methods Programs Biomed. - 2020;186:105198. https://doi.org/10.1016/j.cmpb.2019.105198
22. Khanagar S.B., Al-ehaideb A., Maganur P.C., Vishwanathaiah S., Patil S., Baeshen H.A., Sarode S.C., Bhandi S. Developments, application, and performance of artificial intelligence in dentistry - A systematic review // Journal of dental sciences. - 2021;16(1):508-522. https://doi.org/10.1016/j.jds.2020.06.019
23. Kulkarni S., Seneviratne N., Baig M.S., Khan A.H.A. Artificial Intelligence in Medicine: Where Are We Now? // Acad Radiol. - 2020;27(1):62-70. https://doi.org/10.1016/j.acra.2019.10.001
24. 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 // J Dent. - 2018;77:106-111. https://doi.org/10.1016/j.jdent.2018.07.015
25. Leite A.F., de Faria Vasconcelos K., Willems H., Jacobs R. Radiomics and Machine Learning in Oral Healthcare // Proteomics Clin Appl. - 2020;14(3):e1900040. https://doi.org/10.1002/prca.201900040
26. Leonardi D.K., Dutra K.L., Haas L., Porporatti A.L., Flores-Mir C., Santos J.N., Mezzomo L.A., Corrêa M., De Luca Canto G. Diagnostic Accuracy of Cone-beam Computed Tomography and Conventional Radiography on Apical Periodontitis: A Systematic Review and Meta-analysis // J Endod. - 2016;42(3):356-364. https://doi.org/10.1016/j.joen.2015.12.015
27. Orhan K., Bilgir E., Bayrakdar I.S., Ezhov M., Gusarev M., Shumilov E. Evaluation of artificial intelligence for detecting impacted third molars on cone-beam computed tomography scans // J Stomatol Oral Maxillofac Surg. - 2021;122(4):333-337. https://doi.org/10.1016/j.jormas.2020.12.006
28. Orhan K., Bayrakdar I.S., Ezhov M., Kravtsov A., Özyürek T. Evaluation of artificial intelligence for detecting periapical pathosis on cone-beam computed tomography scans // Int Endod J. - 2020;53(5):680-689. https://doi.org/10.1111/iej.13265
29. Pauwels R., Araki K., Siewerdsen J.H., Thongvigitmanee S.S. Technical aspects of dental CBCT: state of the art // Dentomaxillofac Radiol. - 2015;44(1):20140224. https://doi.org/10.1259/dmfr.20140224
30. Schuhbaeck A., Otaki Y., Achenbach S., Schneider C., Slomka P., Berman D.S., Dey D. Coronary calcium scoring from contrast coronary CT angiography using a semiautomated standardized method // J Cardiovasc Comput Tomogr. - 2015;9(5):446-453. https://doi.org/10.1016/j.jcct.2015.06.001
31. Schwendicke F., Samek W., Krois J. Artificial Intelligence in Dentistry: Chances and Challenges // J Dent Res. - 2020;99(7):769-774. https://doi.org/10.1177/0022034520915714
32. Setzer F.C., Shi K.J., Zhang Z., Yan H., Yoon H., Mupparapu M., Li J. Artificial Intelligence for the Computer-aided Detection of Periapical Lesions in Cone-beam Computed Tomographic Images // J Endod. - 2020;46(7):987-993. https://doi.org/10.1016/j.joen.2020.03.025
33. Xiang., Zhao L., Liu Z., Wu X., Chen J., Long E., Lin D., Zhu Y., Chen C., Lin Z., Lin H. Implementation of artificial intelligence in medicine: Status analysis and development suggestions // Artif Intell Med. - 2020;102:101780. https://doi.org/10.1016/j.artmed.2019.101780
34. Zadrożny Ł., Regulski P., Brus-Sawczuk K., Czajkowska M., Parkanyi L., Ganz S., Mijiritsky E. Artificial Intelligence Application in Assessment of Panoramic Radiographs // Diagnostics. - 2022;12(1):224. https://doi.org/10.3390/diagnostics12010224
