CAN ARTIFICIAL INTELLIGENCE REPLACE CLINICIANS IN PROSTHETIC DENTISTRY?

Abstract

In recent years, artificial intelligence applications in dentistry have become increasingly popular. Study results have been published indicating that these applications have a comparable level of accuracy to clinicians, and they have been shown to assist in the rapid analysis of medical data. Artificial intelligence applications have started to be widely used in all branches of dentistry, especially in prosthodontics. This article discusses the basic features of artificial intelligence technology and provides detailed information on its applications, particularly in the field of prosthetic dentistry. Additionally, information is provided about potential future applications awaiting clinicians and patients using artificial intelligence technology.

Keywords

• Dental prosthesis
• prosthodontics
• artificial intelligence

YAPAY ZEKÂ PROTETİK DİŞ TEDAVİSİNDE KLİNİSYENLERİN YERİNİ ALABİLİR Mİ?

Abstract

Diş hekimliğinde yapay zekâ uygulamaları son yıllarda popüler hale gelmiştir. Bu uygulamaların klinisyenlerle kıyaslanabilir bir doğruluk seviyesine sahip olduğuna ilişkin çalışma sonuçları yayınlanmış ve bu uygulamaların hızlı bir şekilde tıbbi verilerin analiz edilmesine yardımcı olduğu gösterilmiştir. Yapay zekâ uygulamaları başta protetik diş tedavisi olmak üzere diş hekimliğinin tüm branşlarında yaygın olarak kullanılmaya başlanmıştır. Bu makalede, yapay zekâ teknolojisinin temel özelliklerinden bahsedilmiş ve özellikle protetik diş tedavisi alanında kullanıldığı uygulamalara detaylı olarak değinilmiştir. Bunun yanı sıra, gelecekte yapay zekâ teknolojisi kullanılarak klinisyenleri ve hastaları bekleyen potansiyel uygulamalar hakkında bilgi verilmiştir.

Keywords

• Dental protez
• protetik diş tedavisi
• yapay zekâ

References

1. Boreak N. Effectiveness of artificial intelligenc eapplications designed for endodontic diagnosis, decision-making, and prediction of prognosis: A systematic review. J Contemp Dent Pract. 2020;21(8):926-934.doi:10.5005/jp-journals-10024-2894/
2. Khanagar SB, Al-Ehaideb A, Maganur PC, Vishwanathaiah S, Patil S, Baeshen HA, et al. Developments, application, and performance of artificial intelligence in dentistry - A systematic review. J Dent Sci. 2021;16(1):508-522. doi:10.1016/j.jds.2020.06.019
3. Chen RQ, Lee Y, Yan H, et al. Leveraging Pretrained Transformers for Efficient Segmentation and Lesion Detection in Cone-Beam Computed Tomography Scans. J Endod. 2024;50(10):1505-1514.e1. doi:10.1016/j.joen.2024.07.012
4. Orhan K, Bayrakdar I, Ezhov M, Kravtsov A, Özyürek T. Evaluation of artificial intelligence for detecting periapical pathosis on cone‐beam computed tomographys cans. IntEndod J. 2020;53(5):680-689. doi:10.1111/iej.13265
5. Nicolielo LFP, Van Dessel J, Van Lenthe GH, Lambrichts I, Jacobs R. Computer-basedautomatic classification of trabecular bone pattern can asist radiographic bone quality assessment at dental implant site. Brit J Radiol. 2018;91(1092):20180437.doi:10.1259/bjr.20180437
6. Kim DW, Kim J, Kim T, et al. Prediction of hand-wristmaturation stages based on cervical vertebrae images using artificial intelligence. Orthod Craniofac Res. 2021;24(2):68-75.doi:10.1111/ocr.12514
7. Kise Y, Ikeda H, Fujii T, Fukuda M, Ariji Y, Fujita H, et al. Preliminary study on the application of deep learning system to diagnosis of Sjögren's syndrome on CT images. Dentomaxillofac Radiol. 2019;48(6):20190019. doi: 10.1259/dmfr.20190019
8. Ariji Y, Sugita Y, Nagao T, et al. CT evaluation of extranodal extension of cervical lymphno demetastases in patients with oral squamous cellcarcinoma using deep learning classification. Oral Radiol. 2020:36(2);148-155. doi:10.1007/s11282-019-00391-4

9. Zhang W, Li J, Li ZB, Li Z. Predicting post operative facials welling following impacted mandibular third molar sextraction by using artificial neural network sevaluation. Sci Rep. 2018:16;8(1):12281. doi:10.1038/s41598-018-29934-1
10. Altalhi AM, Alharbi FS, Alhodaithy MA, et al. Theimpact of artificial intelligence on dental implantology: A narrative review. Cureus. 2023;15(10):e47941. doi:10.7759/cureus.47941
11. Taborri J, Molinaro L, Russo L, Palmerini V, Larion A, Rossi S. Comparison of machine learning algorithms fed with mobility-related and baropodometric measurements to identify temporo mandibular disorders. Sensors (Basel). 2024;24(11):3646.doi:10.3390/s24113646
12. Vinayahalingam S, Xi T, Bergé S, Maal T, de Jong G. Auto mated detection of third molars and mandibular nevre by deep learning. Sci Rep. 2019;9(1):9007. doi:10.1038/s41598-019-45487-3
13. Nordblom NF, Büttner M, Schwendicke F. Artificial intelligence in orthodontics: Critical review. J Dent Res. 2024;103(6):577-584. doi:10.1177/00220345241235606
14. Chung EJ, Yang BE, Park IY, Yi S, On SW, Kim YH, et al. Effectiveness of cone-beam computed tomography-generated cephalograms using artificial intelligence cephalometric analysis. Sci Rep. 2022;12(1):20585. doi:10.1038/s41598-022-25215-0
15. Umer F, Habib S. Critical analysis of artificial intelligence in endodontics: Ascoping review. J Endod. 2022;48(2):152-160. doi:10.1016/j.joen.2021.11.007
16. Chen RQ, Lee Y, Yan H, et al. Leveraging Pretrained Transformers for Efficient Segmentation and Lesion Detection in Cone-Beam Computed Tomography Scans. J Endod. 2024;50(10):1505-1514.e1. doi:10.1016/j.joen.2024.07.012
17. Prados-Privado M, GarcíaVillalón J, Martínez-Martínez CH, Ivorra C, Prados-Frutos JC. Dental caries diagnosis and detection using neural networks: A systematic review. J Clin Med. 2020;9(11):3579. doi:10.3390/jcm9113579
18. Schwendicke F, Elhennawy K, Paris S, Friebertshäuser P, Krois J. Deep learning for caries lesion detection in near-infrared light transillumination images: A pilot study. J Dent. 2020;92:103260.doi:10.1016/j.jdent.2019.103260
19. Moutselos K, Berdouses E, Oulis C, Maglogiannis I. Recognizing Occlusal Caries in Dental Intraoral Images Using Deep Learning. Annu Int Conf IEEE Eng Med Biol Soc. 2019;2019:1617-1620. doi:10.1109/EMBC.2019.8856553
20. Farhadian M, Shokouhi P,Torkzaban P. A decision support system based on support vector machine for diagnosis of periodontal disease. BMC Res. Notes. 2020;13(1):337.doi:10.1186/s13104-020-05180-5
21. Chen WP, Chang SH, Tang CY, Liou ML, Tsai SJ, Lin YL. Composition Analysis and Feature Selection of the Oral Microbiota Associated with Periodontal Disease. Biomed Res Int. 2018;2018:3130607. doi:10.1155/2018/3130607
22. Li W,Chen Y, Sun W,Brown M,Zhang X, Wang S, et al. Gingivitis identification method based on contrast-limited adaptive histo grame qualization, gray-level co-occurren cematrix, and extreme learning machine. Int. J. Imaging Syst. Technol. 2019;29(1):77-82.doi:10.1002/ima.22298
23. Lee JH, Kim DH, Jeong SN, Choi SH. Diagnosis and prediction of periodontally compromised teeth using a deep learning-based convolution al neural network algorithm. J Periodontal Implant Sci. 2018;48(2):114123.doi:10.5051/jpis.2018.48.2.114
24. Yüce F, Taşsöker M. Diş hekimliğinde yapay zeka uygulamaları. 7 tepe Klinik Dergisi. 2023;19(2):141-149.doi:10.5505/yeditepe.2023.05668
25. Rokhshad R, Zhang P, Mohammad-Rahimi H, Shobeiri P, Schwendicke F. Current applications of artificial intelligence for pediatric dentistry: A systematic review and meta-analysis. Pediatr Dent. 2024;46(1):27-35
26. Shafi N, Bukhari F, Iqbal W, Almustafa KM, Asif M, Nawaz Z. Cleft prediction before birth using deep neural network. Health Inform. J. 2020;26:(4)2568-2585.doi:10.1177/1460458220911789
27. Kuwada C, ArijiY, Kise Y, Funakoshi T, Fukuda M, Kuwada T, et al. Detection and Classification of Unilateral Cleft Alveolus with and without Cleft Palate on Panoramic Radiographs Using a Deep Learning System. Sci. Rep. 2021;11(1):16044. doi:10.1038/s41598-021-95653-9
28. Zhang Y, Pei Y, Chen S, Guo Y, MaG, Xu T, et al. Volumetric registration-based cleft volume estimation of alveolar cleft grafting procedures. In Proceedings of the International Symposium on Biomedical Imaging. 2020;7:99-103.doi:10.1109/ISBI45749.2020.9098407
29. Seo J, Yang IH, Choi JY, Lee JH, Baek SH. Three-Dimensional Facial Soft Tissue Changes After Orthognathic Surgery in Cleft Patients Using Artificial Intelligence-Assisted Landmark Autodigitization. J Craniofac Surg. 2021;32(8):2695-2700. doi:10.1097/SCS.0000000000007712
30. Takahashi T, Nozaki K, Gonda T, Ikebe K. A system for designing removable partial dentures using artificial intelligence. Part 1. Classification of partially edentulous arches using a convolutional neural network. J Prosthodont Res.2021;65(1):115-118.doi:10.2186/jpr.JPOR_2019_354
31. Ateş G. Protetik diş tedavisinde kullanılan yapay zeka uygulamaları 1st Bilsel International Sumela Scientific Researches Congress 22-23 July 2023, Trabzon/Turkey.https://bilselkongreleri.com/panel/uploads/pdf/S%C3%BCmela%20Revize.pdf
32. Ceylan G, Emir F. Estetik ve protetik yapay zekâ uygulamalarında güncel ve gelecek vadeden yaklaşımlar. Turkiye Klinikleri J DentalSci. 2023:38-44.
33. KhanagarSB, Al-Ehaideb A, Maganur PC, et al. Developments, application, and performance of artificial intelligence in dentistry–A systematic review. J Dent Sci. 2021;16(1):508-522.doi:10.1016/j.jds.2020.06.019
34. Koçak Topbaş N. Diş hekimliği alanında uluslararası teori, araştırma ve derlemeler. 1. Basım, Serüven Yayınevi, c2023: 99-110. ISBN: 978-625-6760-23-3
35. Zhang B, Dai N, Tian S, Yuan F, Yu Q. The extraction method of tooth preparation magrin line based on Soctree CNN. Int J Numer Method Biomed Eng. 2019;35(10):e3241 doi:10.1002/cnm.3241
36. Blatz MB, Chiche G, BahatO, Roblee R, Coachman C, Heymann HO. Evolution of a esthetic dentistry. J Dent Res. 2019;98(12):1294-1304. doi:10.1177/0022034519875450
37. Coachman C, Paravina RD. Digitally enhanced esthetic dentistry-from treatment planning to quality control. J Esthet Restor Dent. 2016;28(1):3-4.doi:10.1111/jerd.12205
38. Omar D, Duarte C. The application of parameters for comprehensive smile esthetics by digital smile design programs: A review of literature. Saudi Dent J. 2018;30(1):7-12. doi:10.1016/j.sdentj.2017.09.001
39. Yüzbaşıoğlu E, Albayrak B, Özdemir G. Dijital gülüş tasarımı: öngörülebilir sonuçlar. J ExpClinMed. 2021;38 (3s):123-128.doi:10.52142/omujecm.38.si.dent.8
40. Chen YW, Stanley K, Att W. Artificial intelligence in dentistry: Current applications and future perspectives. Quintessence Int. 2020;51(3):248-257.doi:10.3290/j.qi.a44465
41. Silva BP, Mahn E, Stanley K, Coachman C. The facial flow concept: An organic orofaci alanalysis-the vertical component. J Prosthet Dent. 2019;121(2):189-194. doi:10.1016/j.prosdent.2018.03.023
42. Lerner H, Mouhyi J, Admakin O, Mangano F. Artificial intelligence in fixed implant prosthodontics: Aretrospective study of 106 implant-supported monolithic zirconia crowns inserted in the posterior jaws of 90 patients. BMC oral Health. 2020;20(1):1-16.doi:10.1186/s12903-020-1062-4
43. Jiao T, Zhang F, Huang X, Wang C. Design and fabrication of auricular pros theses by CAD/CAM system. Int J Prosthodont. 2004;17:460-463
44. Agrawal P, Nikhade P. Artificial intelligence in dentistry: past, present, and future. Cureus. 2022;14(7):e27405.doi:10.7759/cureus.27405