İki farklı CAD yazılımına ait yapay zekanın restorasyon öneri ve ayarlama süresinin karşılaştırılması
Abstract
Amaç: Bu in vitro çalışmanın amacı, iki farklı dijital tasarım yazılımına ait yapay zekanın restorasyon önerme sürelerini ve uyumlama sürecini karşılaştırmaktır.
Gereç ve Yöntemler: Üst çene sağ birinci premolar diş, fantom modelde (n=26) zirkonyum tam seramik kron restorasyonu için prepare edildi. Daha sonra, modeller bir intraoral tarayıcı kullanılarak tarandı ve karşıt ark ve sağ ve sol taraflı oklüzyonlar da kaydedildi. Veriler iki farklı bilgisayar destekli tasarım yazılım programına (Grup Exocad ve Grup 3Shape) aktarıldı ve iki tasarım süreci yürütüldü. Her iki sistemde de, tamamen anatomik monolitik zirkonyum kron tasarım süreçleri kütüphane modunda gerçekleştirildi. Yapay zekanın restorasyonu önermek için harcadığı süre ve diş teknisyeninin restorasyonu uyumlamak için harcadığı süre kaydedildi ve hem ayrı ayrı hem de birbirleriyle karşılaştırılarak istatistiksel olarak analiz edildi (P<.05).
Bulgular: Yapay zekanın restorasyon öneri süresi ve diş teknisyeni restorasyon ayarlama süresi değerleri arasında farklı yazılımlara göre istatistiksel olarak anlamlı farklar bulundu (P<.05). Grup Exocad, Grup 3Shape'ten daha düşük değerler gösterdi (P<.001).
Sonuçlar: Exocad, 3Shape Dental Sistemine kıyasla daha hızlı ve kolay bir restorasyon tasarımı sağlar.
Keywords
References
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- 4. Singi SR, Sathe S, Reche AR, Sibal A, Mantri N. Extended Arm of Precision in Prosthodontics: Artificial Intelligence. Cureus. 2022;14:e30962.
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- 6. Aydiner SF, Duymus ZY, Yanikoglu N. Investigation of the Effect of Mouthwash on Bonding Temporary Crown Materials with Various Temporary Cements. Cumhur Dent J. 2023;26:359-366.
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- 34. Yuan F, Dai N, Tian S, et al. Personalized design technique for the dental occlusal surface based on conditional generative adversarial networks. Int j numer method biomed eng. 2020;36:e3321.
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- 40. Bae E-B, Cho W-T, Park D-H, et al. Comparison of fit and trueness of zirconia crowns fabricated by different combinations of open CAD-CAM systems. J Adv Prosthodont. 2023;15:155-170.
Comparison of Restoration Recommendation and Adjustment Time of AI Belong to Two Different CAD Software
Abstract
Objectives: The purpose of the in vitro study is to compare the restoration recommendation time and adjustment process of artificial intelligence (AI) belonging to two different digital design software.
Materials and Methods: The maxillary right first premolar tooth was prepared for zirconia all-ceramic crown restoration in a phantom model (n=26). Then, models were scanned using an intraoral scanner, and the opposing arch, and the right- and left-sided occlusions were also recorded. Data were transferred to two computer-aided design software programs (Group Exocad and Group 3Shape) and two design processes were used. In both systems, fully anatomical monolithic zirconia crown design processes were carried out in library mode. The time taken by the AI to recommend the restoration and the time the dental technician spent adjusting the restoration were recorded and were analyzed statistically, both separately and in comparison, to one another (P<.05).
Results: Statistically significant differences were found between the values of AI restoration recommendation time and dental technician restoration adjustment time according to the different software designs (P<.05). Group Exocad showed lower values than Group 3Shape (P<.001).
Conclusions: Exocad provides a faster and easier restoration design in comparison with the 3Shape Dental System.
Keywords
Ethical Statement
Ethical approval was not required for this in vitro study.
References
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- 2. Dwivedi YK, Hughes L, Ismagilova E, et al. Artificial Intelligence (AI): Multidisciplinary perspectives on emerging challenges, opportunities, and agenda for research, practice and policy. Int J Inf Manage. 2021;57:101994.
- 3. Tandon D, Rajawat J. Present and future of artificial intelligence in dentistry. J oral Biol craniofacial Res. 2020;10:391-396.
- 4. Singi SR, Sathe S, Reche AR, Sibal A, Mantri N. Extended Arm of Precision in Prosthodontics: Artificial Intelligence. Cureus. 2022;14:e30962.
- 5. Taşın S, Güvenir M, Ismatullaev A. Effects of Surface Characteristics of Conventionally Manufactured, CAD/CAM Milled, and 3D-Printed Interim Materials on Adherence of Streptococcus Mutans and Candida Albicans. Cumhur Dent J. 2023;26:227-234.
- 6. Aydiner SF, Duymus ZY, Yanikoglu N. Investigation of the Effect of Mouthwash on Bonding Temporary Crown Materials with Various Temporary Cements. Cumhur Dent J. 2023;26:359-366.
- 7. Abdalla-Aslan R, Yeshua T, Kabla D, Leichter I, 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;130:593-602.
- 8. Wang F, Zhang T, Zhou Q, Lu Y. Comparison of the morphological accuracy of automatic crowns designed by multiple computer-aided design software programs with different levels of dentition information acquisition. J Prosthet Dent. 2024;132:441-452.
- 9. Zhang R, Ding Q, Sun Y, Zhang L, Xie Q. Assessment of CAD-CAM zirconia crowns designed with 2 different methods: A self-controlled clinical trial. J Prosthet Dent. 2018;120:686-692.
- 10. Reiss B. Occlusal surface design with Cerec 3D. Int J Comput Dent. 2003;6:333-342.
- 11. Chen D, Yu M-Q, Li Q-J, He X, Liu F, Shen J-F. Precise tooth design using deep learning-based templates. J Dent. 2024;144:104971.
- 12. Schenk O. Biogeneric--another step closer to nature. V3.8: largest update since introduction of the 3D software. Int J Comput Dent. 2010;13:169-174.
- 13. Jedynakiewicz NM, Martin N. Functionally-generated pathway theory, application and development in Cerec restorations. Int J Comput Dent. 2001;4:25-36.
- 14. Wu Z, Zhang C, Ye X, et al. Comparison of the Efficacy of Artificial Intelligence-Powered Software in Crown Design: An In Vitro Study. Int Dent J. Published online July 2024.
- 15. Liu C-M, Lin W-C, Lee S-Y. Evaluation of the efficiency, trueness, and clinical application of novel artificial intelligence design for dental crown prostheses. Dent Mater. 2024;40:19-27.
- 16. Chau RCW, Hsung RT-C, McGrath C, Pow EHN, Lam WYH. Accuracy of artificial intelligence-designed single-molar dental prostheses: A feasibility study. J Prosthet Dent. 2024;131:1111-1117.
- 17. Li L, Chen H, Li W, Wang Y, Sun Y. The Effect of Residual Dentition on the Dynamic Adjustment of Wear Facet Morphology on a Mandibular First Molar Crown. J Prosthodont Off J Am Coll Prosthodont. 2021;30:351-355.
- 18. Raith S, Vogel EP, Anees N, et al. Artificial Neural Networks as a powerful numerical tool to classify specific features of a tooth based on 3D scan data. Comput Biol Med. 2017;80:65-76.
- 19. Revilla-León M, Gómez-Polo M, Vyas S, et al. Artificial intelligence models for tooth-supported fixed and removable prosthodontics: A systematic review. J Prosthet Dent. 2023;129:276-292.
- 20. Zhang B, Dai N, Tian S, Yuan F, Yu Q. The extraction method of tooth preparation margin line based on S-Octree CNN. Int j numer method biomed eng. 2019;35:e3241.
- 21. Wu C-H, Tsai W-H, Chen Y-H, Liu J-K, Sun Y-N. Model-Based Orthodontic Assessments for Dental Panoramic Radiographs. IEEE J Biomed Heal informatics. 2018;22:545-551.
- 22. Son LH, Tuan TM, Fujita H, et al. Dental diagnosis from X-Ray images: An expert system based on fuzzy computing. Biomed Signal Process Control. 2018;39:64-73.
- 23. Khanagar SB, Al-Ehaideb A, Maganur PC, et al. Developments, application, and performance of artificial intelligence in dentistry - A systematic review. J Dent Sci. 2021;16:508-522.
- 24. Wang J, Chen F, Ma Y, et al. XBound-Former: Toward Cross-Scale Boundary Modeling in Transformers. IEEE Trans Med Imaging. 2023;42:1735-1745.
- 25. Lai Y, Fan F, Wu Q, et al. LCANet: Learnable Connected Attention Network for Human Identification Using Dental Images. IEEE Trans Med Imaging. 2021;40:905-915.
- 26. Bhatia A, Tiwari S. Acta Scientific Dental Sciences (ISSN: 2581-4893) Artificial Intelligence: An Advancing Front of Dentistry. Published online September 25, 2020. doi:10.31080/ASDS.2019.03.0714
- 27. Cho J-H, Çakmak G, Yi Y, Yoon H-I, Yilmaz B, Schimmel M. Tooth morphology, internal fit, occlusion and proximal contacts of dental crowns designed by deep learning-based dental software: A comparative study. J Dent. 2024;141:104830.
- 28. Capobianco V, Baroudi K, Santos MJMC, et al. Post-fatigue fracture load, stress concentration and mechanical properties of feldspathic, leucite- and lithium disilicate-reinforced glass ceramics. Heliyon. 2023;9:e17787.
- 29. Hartung F, Kordass B. Comparison of the contact surface pattern between virtual and milled Cerec 3D full-ceramic crowns. Int J Comput Dent. 2006;9:129-136.
- 30. Ender A, Mörmann WH, Mehl A. Efficiency of a mathematical model in generating CAD/CAM-partial crowns with natural tooth morphology. Clin Oral Investig. 2011;15:283-289.
- 31. Ding H, Wu J, Zhao W, Matinlinna JP, Burrow MF, Tsoi JKH. Artificial intelligence in dentistry—A review. Front Dent Med. 2023;4.
- 32. Mrugalska B, Dovramadjiev T, Pavlova D, et al. Open source systems and 3D computer design applicable in the dental medical engineering Industry 4.0 – sustainable concept. Procedia Manuf. 2021;54:296-301.
- 33. Farook TH, Ahmed S, Jamayet N Bin, et al. Computer-aided design and 3-dimensional artificial/convolutional neural network for digital partial dental crown synthesis and validation. Sci Rep. 2023;13:1561.
- 34. Yuan F, Dai N, Tian S, et al. Personalized design technique for the dental occlusal surface based on conditional generative adversarial networks. Int j numer method biomed eng. 2020;36:e3321.
- 35. Herbert T. Shillingburg Jr, D. A. S., Edwin L. Wilson Jr, Joseph R. Cain, Donald L. Mitchell LJB& JCK. Fundamentals of Fixed Prosthodontics. 4th ed. Quintessence Pub Co.; 2012.
- 36. Li X, Wang X, Chen M. Accurate extraction of outermost biological characteristic curves in tooth preparations with fuzzy regions. Comput Biol Med. 2018;103:208-219.
- 37. Hosny A, Parmar C, Quackenbush J, Schwartz LH, Aerts HJWL. Artificial intelligence in radiology. Nat Rev Cancer. 2018;18:500-510.
- 38. Syed-Abdul S, Fernandez-Luque L, Jian W-S, et al. Misleading health-related information promoted through video-based social media: anorexia on YouTube. J Med Internet Res. 2013;15:e30.
- 39. Shan T, Tay FR, Gu L. Application of Artificial Intelligence in Dentistry. J Dent Res. 2021;100:232-244.
- 40. Bae E-B, Cho W-T, Park D-H, et al. Comparison of fit and trueness of zirconia crowns fabricated by different combinations of open CAD-CAM systems. J Adv Prosthodont. 2023;15:155-170.
Details
| Primary Language | English |
|---|---|
| Subjects | Prosthodontics, Dentistry (Other) |
| Journal Section | Original Research Articles |
| Authors | |
| Publication Date | June 30, 2025 |
| Submission Date | January 27, 2025 |
| Acceptance Date | May 12, 2025 |
| Published in Issue | Year 2025Volume: 28 Issue: 2 |
Cumhuriyet Dental Journal (Cumhuriyet Dent J, CDJ) is the official publication of Cumhuriyet University Faculty of Dentistry. CDJ is an international journal dedicated to the latest advancement of dentistry. The aim of this journal is to provide a platform for scientists and academicians all over the world to promote, share, and discuss various new issues and developments in different areas of dentistry. First issue of the Journal of Cumhuriyet University Faculty of Dentistry was published in 1998. In 2010, journal's name was changed as Cumhuriyet Dental Journal. Journal’s publication language is English.
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