The Role of Artificial Intelligence in Prosthetic Dentistry

Bülent Kadir Tartuk

Derleme

Yapay Zekânın Protetik Diş Hekimliğindeki Rolü

Yıl 2025, Cilt: 7 Sayı: 2, 71 - 87, 30.06.2025

Bülent Kadir Tartuk

Öz

Yapay zekâya (YZ) dayalı diş hekimliği, ilerleyen teknoloji ile gerçeğe dönüşüyor. YZ, tıp ve diş hekimliğinin birçok alanında kullanılmaktadır. Akıllı insan davranışını taklit etmek için makineleri kullanan bir teknolojidir. Zekâ inovasyonu alanındaki önemli etkisi ve atılımı nedeniyle dünya çapında popülerlik kazanmaktadır. Diş hekimliğinde, özellikle protetik diş hekimliğinde hayat kurtarıcıdır; çünkü protezlerin tasarım ve üretimine, renk ve materyal seçimine, maksilyofasiyal protezlerin elde edilmesine yardımcı olmaktadır. Ayrıca hasta dokümantasyonu, teşhis, tedavi planlaması, hasta yönetimi ve diş hekimliği eğitim süreçleri gibi birçok alanda diş hekimlerinin iş yüküne yardımcı olur, klinisyen ve araştırmacıların daha çok çalışmak yerine daha akıllıca çalışmasını sağlar. YZ, diş hekiminin yerini alamasa da hastaya en doğru tedaviyi seçeneğini sunmada dişhekimine yardımcı olurs. YZ ve dijitalleşmenin entegrasyonu, son derece umut verici beklentilere sahip yeni bir diş hekimliği paradigması getirmiştir. Yetersiz ve yanlış verilerin mevcudiyeti artık YZ'nin efektif kullanımının önündeki tek engeldir. Bu nedenle, diş hekimleri ve klinisyenler, yakın zamanda YZ’yi kullanacak olan veri tabanlarında gerçek verileri toplamaya ve girmeye odaklanmalıdır. Bu çalışma, protetik diş hekimliğinde YZ'nin çeşitli uygulamalarına, sınırlamalarına ve gelecekteki kapsamına odaklanmaktadır.

Anahtar Kelimeler

yapay zekâ, protetik diş tadevisi, makine öğrenimi.

Kaynakça

1. Holzinger A, Langs G, Denk H, Zatloukal K, Müller H. Causability and explainability of artificial intelligence in medicine. Wiley Interdiscip Rev Data Min Knowl Discov. 2019 Jul-Aug;9(4):e1312.
2. Bhatia A.P, Tiwari S. Artificial intelligence: an advancing front of dentistry. Acta Sci Dent Sci, 2019;3:135-8.
3. Khanna SS, Dhaimade PA . Artificial intelligence: transforming dentistry today. Indian J Basic Appl Med Res, 2017;6.3: 161-167.
4. McCarthy J. Artificial intelligence, logic, and formalising common sense. Machine Learning and the City: Applications in Architecture and Urban Design. 2022;69-90.
5. Samuel AL. Some studies in machine learning using the game of checkers. IBM J Res Dev. 1959;3(3):210-29
6. Howard J. Artificial intelligence: Implications for the future of work. Am J Ind Med. 2019;62(11):917-26.
7. Singi SR, Sathe S, Reche AR, Sibal A, Mantri N. Extended Arm of Precision in Prosthodontics: Artificial Intelligence. Cureus. 2022 Nov 1;14(11):e30962.
8. Chen YW, Stanley K, Att W. Artificial intelligence in dentistry: current applications and future perspectives. Quintessence Int. 2020;51(3):248-257.
9. Rekow ED. Digital dentistry: The new state of the art - Is it disruptive or destructive? Dent Mater. 2020 Jan;36(1):9-24.
10. Khanagar SB, Al-Ehaideb A, Maganur PC, Vishwanathaiah S, Patil S, Baeshen HA, Sarode SC, Bhandi S. Developments, application, and performance of artificial intelligence in dentistry - A systematic review. J Dent Sci. 2021 Jan;16(1):508-522.
11. Ahmed N, Abbasi MS, Zuberi F, Qamar W, Halim MSB, Maqsood A, Alam MK. Artificial Intelligence Techniques: Analysis, Application, and Outcome in Dentistry-A Systematic Review. Biomed Res Int. 2021 Jun 22;2021:9751564. 12. Mohri M, Rostamizadeh A, Talwalker A. Foundations of Machine Learning. 2nd ed. Cambridge, MA: MIT Press; 2018
13. Meyers AR, Al-Tarawneh IS, Wurzelbacher SJ, Bushnell PT, Lampl MP, Bell JL, Bertke SJ, Robins DC, Tseng CY, Wei C, Raudabaugh JA, Schnorr TM. Applying Machine Learning to Workers' Compensation Data to Identify Industry-Specific Ergonomic and Safety Prevention Priorities: Ohio, 2001 to 2011. J Occup Environ Med. 2018 Jan;60(1):55-73.
14. Choy G, Khalilzadeh O, Michalski M, et al. Current applications and future impact of machine learning in radiology. Radiology. 2018;288(2):318‐328.
15. Hinton G, Sejnowski TJ. Unsupervised Learning: Foundations of Neural Computation. Cambridge, MA: MIT Press; 1999.
16. James G, Witten D, Hastic T, Tibshirani R. An Introduction to Statistical Learning. New York, NY: Springer Science; 2017.
17. Ng A. What artificial intelligence can and can’t right now. Harv Bus Rev. 2016.
18. Chapelle O, Schölkopf B, Zein A. Semi‐supervised Learning. Cambridge, MA: MIT Press. 2006
19. Kelleher JD, Tierney B. Data Science. Cambridge, MA: MIT Press. 2018.
20. Johnson, K. Alexa scientists reduce speech recognition errors up to 22% with semi-supervised learning. Ventur. Beat. 2019
21. Varian HR. Artificial intelligence, economics, and industrial organization. Cambridge, MA, USA: National Bureau of Economic Research. 2018.
22. Knight W. 10 breakthrough technologies: Reinforcement learning. 2017.
23. McCulloch WS, Pitts W. A logical calculus of the ideas immanent in nervous activity. 1943. Bull Math Biol. 1990;52(1-2):99-115; discussion 73-97.
24. Aggrawal CC. Neural Networks and Deep Learning. A Textbook. New York, NY: Springer. 2018.
25. Chuang CL, Huang ST. A hybrid neural network approach for credit scoring. Expert Systems. 2011;28.2:185-196.
26. Zarra T, Galang MG, Ballesteros F Jr, Belgiorno V, Naddeo V. Environmental odour management by artificial neural network - A review. Environ Int. 2019 Dec;133(Pt B):105189.
27. Le TH. Applying artificial neural networks for face recognition. Advances in Artificial Neural Systems. 2011;1:673016.
28. Scotti L, Ishiki H, Mendonça Júnior FJ, da Silva MS, Scotti MT. Artificial Neural Network Methods Applied to Drug Discovery for Neglected Diseases. Comb Chem High Throughput Screen. 2015;18(8):819-29.
29. Krizhevsky A, Sutskever I, Hinton G. ImageNet classification with deep convolutional networks. Proceedings of Advances in Neural Information Processing Systems. 2012;25:1090‐1098.
30. Kim M, Yun J, Cho Y, Shin K, Jang R, Bae HJ, Kim N. Deep Learning in Medical Imaging. Neurospine. 2019 Dec;16(4):657-668
31. Carlsson GE, Omar R. Trends in prosthodontics. Med Princ Pract. 2006;15(3):167-79.
32. Chau RCW, Hsung RT, McGrath C, Pow EHN, Lam WYH. Accuracy of artificial intelligence-designed single-molar dental prostheses: A feasibility study. J Prosthet Dent. 2024 Jun;131(6):1111-1117.
33. Sikri A, Sikri J, Gupta R. Artificial intelligence in prosthodontics and oral implantology—a narrative review. Glob Acad J Dent Oral Health. 2023;5(2):13-9.
34. Leitão CIMB, Fernandes GVO, Azevedo LPP, Araújo FM, Donato H, Correia ARM. Clinical performance of monolithic CAD/CAM tooth-supported zirconia restorations: systematic review and meta-analysis. J Prosthodont Res. 2022;66:374–84
35. Liu CM, Lin WC, Lee SY. Evaluation of the efficiency, trueness, and clinical application of novel artificial intelligence design for dental crown prostheses. Dent Mater. 2024;40:19–27.
36. Chen Y, Lee JKY, Kwong G, Pow EHN, Tsoi JKH. Morphology and fracture behavior of lithium disilicate dental crowns designed by human and knowledge-based AI. J Mech Behav Biomed Mater. 2022;131:105256.
37. Cho JH, Yi Y, Choi J, Ahn J, Yoon HI, Yilmaz B. Time efficiency, occlusal morphology, and internal fit of anatomic contour crowns designed by dental software powered by generative adversarial network: a comparative study. J Dent. 2023;138:104739.
38. Teng TY, Wu JH, Lee CY. Acceptance and experience of digital dental technology, burnout, job satisfaction, and turnover intention for Taiwanese dental technicians. BMC Oral Health. 2022;22:342.
39. Litzenburger AP, Hickel R, Richter MJ, Mehl AC, Probst FA. Fully automatic CAD design of the occlusal morphology of partial crowns compared to dental technicians’ design. Clin Oral Invest. 2013;17:491–6.
40. 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 Oct;35(10):e3241.
41. Jreige CS, Kimura RN, Segundo ÂRTC, Coachman C, Sesma N. Esthetic treatment planning with digital animation of the smile dynamics: A technique to create a 4-dimensional virtual patient. J Prosthet Dent. 2022 Aug;128(2):130-138.
42. Pareek, M, Kaushik B. Artificial intelligence in prosthodontics: a scoping review on current applications and future possibilities. Int J Adv Med. 2022;9(3):367-70.
43. Alexander B, John S, Aralamoodu PO. Artificial intelligence in dentistry: Current concepts and a peep into the future. Int J Adv Res. 2008;6(12):1105-1108.
44. Larson TD. The clinical significance of marginal fit. Northwest Dent J. 2012;91:22.
45. Mai HN, Han JS, Kim HS, Park YS, Park JM, Lee DH. Reliability of automatic finish line detection for tooth preparation in dental computer aided software. J Prosthodont Res. 2023;67:138–43.
46. Choi J, Ahn J, Park JM. Deep learning-based automated detection of the dental crown finish line: an accuracy study. J Prosthet Dent. 2023;S0022–3913(23):00769–2.
47. Denli N, Uludağ B, Kılıçarslan MA, Özkan T. Resistance of artificial acrylic resin teeth to staining. Türkiye Klin Dişhek Bil Derg 1996; 2: 38-42.
48. Köksal T, Dikbas I. Color stability of different denture teeth materials against various staining agents. Dent Mater J 2008; 27: 139-144.
49. Imamura, S, Takahashi H, Hayakawa I, Loyaga-Rendon PG, Minakuchi S. Effect of filler type and polishing on the discoloration of composite resin artificial teeth. Dent Mater J 2008; 27: 802-808.
50. Kawano F, Ohguri T, Ichikawa T, Mizuno I, Hasegawa A. Shock absorbability and hardness of commercially available denture teeth. Int J Prosthodont 2002; 15: 243-247.
51. Zheng K, Liu HJ. Investigation on wear prediction model of dental restoration material based on ensemble learning. Mater Res Innov 2014; 18: 987-991.
52. Özkan G, Aliplik Akın B, Özkan G. The prediction of chemical oxygen demand (cod) or suspended solid (ss) removal using statistical methods and the artificial neural network in the sugar industrial wastewaters. J Eng Appl Sci 2013; 8: 978- 983.
53. Deniz ST, Ozkan P, Ozkan G. The accuracy of the prediction models for surface roughness and micro hardness of denture teeth. Dent Mater J. 2019 Dec 1;38(6):1012-1018.
54. Matin I, Hadzistevic M, Vukelic D, Potran M, Brajlih T. Development of an expert system for the simulation model for casting metal substructure of a metal-ceramic crown design. Comput Methods Programs Biomed. 2017 Jul;146:27-35.
55. Bauer FX, Gau D, Guell F, Eblenkamp M, Loeffelbein DJ. Automated detection of alveolar arches for nasoalveolar molding in cleft lip and palate treatment. Curr Dir Biomed Eng. 2016;2:701–5.
56. Ghods K, Azizi A, Jafari A, Ghods K. Application of Artificial Intelligence in Clinical Dentistry, a Comprehensive Review of Literature. J Dent (Shiraz). 2023 Dec 1;24(4):356-371.
57. Wei J, Peng M, Li Q, Wang Y. Evaluation of a Novel Computer Color Matching System Based on the Improved Back-Propagation Neural Network Model. J Prosthodont. 2018
58. Liu L, Zhou R, Yuan S, Sun Z, Lu X, Li J, et al. Simulation training for ceramic crown preparation in the dental setting using a virtual educational system. Eur J Dent Educ. 2020;24:199–206.
59. Shillingburg HT, Hobo S, Whitsett LD, Jacobi RBS. Fundamentals of fixed prosthodontics 3rd edition 1997.
60. Kateeb ET, Kamal MS, Kadamani AM, Abu Hantash RO, Abu Arqoub MM. Utilising an innovative digital software to grade pre-clinical crown preparation exercise. Eur J Dent Educ. 2017;21:220–7.
61. Feil PH, Gatti JJ. Validation of a motor skills performance theory with applications for dental education. J Dent Educ. 1993;57:628–33.
62. Han S, Yi Y, Revilla-León M, Yilmaz B, Yoon HI. Feasibility of softwarebased assessment for automated evaluation of tooth preparation for dental crown by using a computational geometric algorithm. Sci Rep. 2023;13:11847.
63. Schepke U, Meijer HJ, Vermeulen KM, Raghoebar GM, Cune MS. Clinical bonding of resin nano ceramic restorations to zirconia abutments: a case series within a randomized clinical trial. Clin Implant Dent Relat Res. 2016;18:984–92.
64. Rosentritt M, Preis V, Behr M, Krifka S. In-vitro performance of cad/cam crowns with insufficient preparation design. J Mech Behav Biomed Mater. 2019;90:269–74.
65. Yang Y, Yang Z, Zhou J, Chen L, Tan J. Effect of tooth preparation design on marginal adaptation of composite resin CAD-CAM onlays. J Prosthet Dent. 2020;124:88–93.
66. Yamaguchi S, Lee C, Karaer O, Ban S, Mine A, Imazato S. Predicting the debonding of cad/cam composite resin crowns with ai. J Dent Res. 2019;98:1234–8.
67. Shen KL, Huang CL, Lin YC, Du JK, Chen FL, Kabasawa Y, Chen CC, Huang HL. Effects of artificial intelligence-assisted dental monitoring intervention in patients with periodontitis: A randomized controlled trial. J Clin Periodontol. 2022 Oct;49(10):988-998.
68. Javaid M, Haleem A, Pratap Singh R, Suman R. Pedagogy and innovative care tenets in COVID-19 pandemic: An enhancive way through Dentistry 4.0. Sens Int. 2021;2:100118.
69. Lahoud P, Jacobs R, Boisse P, EzEldeen M, Ducret M, Richert R. Precision medicine using patient-specific modelling: state of the art and perspectives in dental practice. Clin Oral Investig. 2022 Aug;26(8):5117-5128.
70. Lee JH, Jeong SN. Efficacy of deep convolutional neural network algorithm for the identification and classification of dental implant systems, using panoramic and periapical radiographs: A pilot study. Medicine (Baltimore). 2020 Jun 26;99(26):e20787.
71. Ali IE, Tanikawa C, Chikai M, Ino S, Sumita Y, Wakabayashi N. Applications and performance of artificial intelligence models in removable prosthodontics: A literature review. J Prosthodont Res. 2024 Jul 8;68(3):358-367.
72. Ayodele TO. Types of machine learning algorithms. In: Zhang Y, editor. New Advances in Machine Learning, 2010, p. 19-48.
73. Chen Q, Lin S, Wu J, Lyu P, Zhou Y. Automatic drawing of customized removable partial denture diagrams based on textual design for the clinical decision support system. J Oral Sci. 2020;62:236–8.
74. Revilla-León M, Gómez-Polo M, Vyas S, Barmak AB, Gallucci GO, Att W, et al. Artificial intelligence models for tooth-supported fixed and removable prosthodontics: A systematic review. J Prosthet Dent. 2023;129:276–92.
75. Modgil S, Hutton TJ, Hammond P, Davenport JC. Combining biometric and symbolic models for customised, automated prosthesis design. Artif Intell Med. 2002;25:227–45.
76. Ariani N, Visser A, van Oort RP, et al.: Current state of craniofacial prosthetic rehabilitation . Int J Prosthodont. 2013, 26:57-67.
77. Susic I, Travar M, Susic M: The application of CAD/CAM technology in dentistry . IOP Conf Ser: Mater Sci Eng. 2017, 200:12-20.
78. Ciocca L, Mingucci R, Gassino G, Scotti R: CAD/CAM ear model and virtual construction of the mold. J Prosthet Dent. 2007, 98:339-43
79. Runte C, Dirksen D, Deleré H : Optical data acquisition for computer-assisted design of facial prostheses . Int J Prosthodont. 2002, 15:129-32.
80. Jiao T, Zhang F, Huang X, Wang C: Design and fabrication of auricular prostheses by CAD/CAM system . Int J Prosthodont. 2004, 17:460-3.
81. Kurt M, Kurt Z, Işık Ş. Using deep learning approaches for coloring silicone maxillofacial prostheses: A comparison of two approaches. J Indian Prosthodont Soc. 2023;23:84–9.
82. Jiao T, Zhang F, Huang X, Wang C. Design and fabrication of auricular prostheses by CAD/CAM system. Int J Prosthodont. 2004 Jul-Aug;17(4):460-3.
83. Kirubarajan A, Young D, Khan S, Crasto N, Sobel M, Sussman D. Artificial Intelligence and Surgical Education: A Systematic Scoping Review of Interventions. J Surg Educ. 2022 Mar-Apr;79(2):500-515.

The Role of Artificial Intelligence in Prosthetic Dentistry

Yıl 2025, Cilt: 7 Sayı: 2, 71 - 87, 30.06.2025

Bülent Kadir Tartuk

Öz

Artificial intelligence (AI)-based dentistry is becoming a reality with advancing technology. AI is widely used in medicine and dentistry, enabling machines to simulate human intelligence and decision-making processes. Its growing popularity stems from its transformative impact and breakthroughs in intelligent innovation. In dentistry, AI is particularly transformative in prosthetic dentistry, as it assists in the design and production of dentures, colour and material selection, and the fabrication of maxillofacial prostheses. Additionally, it reduces dentist’s workload in areas such as patient documentation, diagnosis, treatment planning, patient management, and dental education, enabling clinicians and researchers to work more efficiently. While AI cannot replace dentists, it supports them in offering the most appropriate treatment options to patients. The integration of AI and digitalization has introduced a new paradigm in dentistry, offering highly promising prospects. However, the primary obstacle to the effective use of AI is the availability of insufficient or inaccurate data. Therefore, dentists and clinicians should prioritize collecting and inputting accurate data into databases that will support AI applications in the near future. This study focuses on the various applications, limitations, and future scope of AI in prosthetic dentistry.

Anahtar Kelimeler

artificial intelligence, prosthodontics treatment, machine learning.

Kaynakça

1. Holzinger A, Langs G, Denk H, Zatloukal K, Müller H. Causability and explainability of artificial intelligence in medicine. Wiley Interdiscip Rev Data Min Knowl Discov. 2019 Jul-Aug;9(4):e1312.
2. Bhatia A.P, Tiwari S. Artificial intelligence: an advancing front of dentistry. Acta Sci Dent Sci, 2019;3:135-8.
3. Khanna SS, Dhaimade PA . Artificial intelligence: transforming dentistry today. Indian J Basic Appl Med Res, 2017;6.3: 161-167.
4. McCarthy J. Artificial intelligence, logic, and formalising common sense. Machine Learning and the City: Applications in Architecture and Urban Design. 2022;69-90.
5. Samuel AL. Some studies in machine learning using the game of checkers. IBM J Res Dev. 1959;3(3):210-29
6. Howard J. Artificial intelligence: Implications for the future of work. Am J Ind Med. 2019;62(11):917-26.
7. Singi SR, Sathe S, Reche AR, Sibal A, Mantri N. Extended Arm of Precision in Prosthodontics: Artificial Intelligence. Cureus. 2022 Nov 1;14(11):e30962.
8. Chen YW, Stanley K, Att W. Artificial intelligence in dentistry: current applications and future perspectives. Quintessence Int. 2020;51(3):248-257.
9. Rekow ED. Digital dentistry: The new state of the art - Is it disruptive or destructive? Dent Mater. 2020 Jan;36(1):9-24.
10. Khanagar SB, Al-Ehaideb A, Maganur PC, Vishwanathaiah S, Patil S, Baeshen HA, Sarode SC, Bhandi S. Developments, application, and performance of artificial intelligence in dentistry - A systematic review. J Dent Sci. 2021 Jan;16(1):508-522.
11. Ahmed N, Abbasi MS, Zuberi F, Qamar W, Halim MSB, Maqsood A, Alam MK. Artificial Intelligence Techniques: Analysis, Application, and Outcome in Dentistry-A Systematic Review. Biomed Res Int. 2021 Jun 22;2021:9751564. 12. Mohri M, Rostamizadeh A, Talwalker A. Foundations of Machine Learning. 2nd ed. Cambridge, MA: MIT Press; 2018
13. Meyers AR, Al-Tarawneh IS, Wurzelbacher SJ, Bushnell PT, Lampl MP, Bell JL, Bertke SJ, Robins DC, Tseng CY, Wei C, Raudabaugh JA, Schnorr TM. Applying Machine Learning to Workers' Compensation Data to Identify Industry-Specific Ergonomic and Safety Prevention Priorities: Ohio, 2001 to 2011. J Occup Environ Med. 2018 Jan;60(1):55-73.
14. Choy G, Khalilzadeh O, Michalski M, et al. Current applications and future impact of machine learning in radiology. Radiology. 2018;288(2):318‐328.
15. Hinton G, Sejnowski TJ. Unsupervised Learning: Foundations of Neural Computation. Cambridge, MA: MIT Press; 1999.
16. James G, Witten D, Hastic T, Tibshirani R. An Introduction to Statistical Learning. New York, NY: Springer Science; 2017.
17. Ng A. What artificial intelligence can and can’t right now. Harv Bus Rev. 2016.
18. Chapelle O, Schölkopf B, Zein A. Semi‐supervised Learning. Cambridge, MA: MIT Press. 2006
19. Kelleher JD, Tierney B. Data Science. Cambridge, MA: MIT Press. 2018.
20. Johnson, K. Alexa scientists reduce speech recognition errors up to 22% with semi-supervised learning. Ventur. Beat. 2019
21. Varian HR. Artificial intelligence, economics, and industrial organization. Cambridge, MA, USA: National Bureau of Economic Research. 2018.
22. Knight W. 10 breakthrough technologies: Reinforcement learning. 2017.
23. McCulloch WS, Pitts W. A logical calculus of the ideas immanent in nervous activity. 1943. Bull Math Biol. 1990;52(1-2):99-115; discussion 73-97.
24. Aggrawal CC. Neural Networks and Deep Learning. A Textbook. New York, NY: Springer. 2018.
25. Chuang CL, Huang ST. A hybrid neural network approach for credit scoring. Expert Systems. 2011;28.2:185-196.
26. Zarra T, Galang MG, Ballesteros F Jr, Belgiorno V, Naddeo V. Environmental odour management by artificial neural network - A review. Environ Int. 2019 Dec;133(Pt B):105189.
27. Le TH. Applying artificial neural networks for face recognition. Advances in Artificial Neural Systems. 2011;1:673016.
28. Scotti L, Ishiki H, Mendonça Júnior FJ, da Silva MS, Scotti MT. Artificial Neural Network Methods Applied to Drug Discovery for Neglected Diseases. Comb Chem High Throughput Screen. 2015;18(8):819-29.
29. Krizhevsky A, Sutskever I, Hinton G. ImageNet classification with deep convolutional networks. Proceedings of Advances in Neural Information Processing Systems. 2012;25:1090‐1098.
30. Kim M, Yun J, Cho Y, Shin K, Jang R, Bae HJ, Kim N. Deep Learning in Medical Imaging. Neurospine. 2019 Dec;16(4):657-668
31. Carlsson GE, Omar R. Trends in prosthodontics. Med Princ Pract. 2006;15(3):167-79.
32. Chau RCW, Hsung RT, McGrath C, Pow EHN, Lam WYH. Accuracy of artificial intelligence-designed single-molar dental prostheses: A feasibility study. J Prosthet Dent. 2024 Jun;131(6):1111-1117.
33. Sikri A, Sikri J, Gupta R. Artificial intelligence in prosthodontics and oral implantology—a narrative review. Glob Acad J Dent Oral Health. 2023;5(2):13-9.
34. Leitão CIMB, Fernandes GVO, Azevedo LPP, Araújo FM, Donato H, Correia ARM. Clinical performance of monolithic CAD/CAM tooth-supported zirconia restorations: systematic review and meta-analysis. J Prosthodont Res. 2022;66:374–84
35. Liu CM, Lin WC, Lee SY. Evaluation of the efficiency, trueness, and clinical application of novel artificial intelligence design for dental crown prostheses. Dent Mater. 2024;40:19–27.
36. Chen Y, Lee JKY, Kwong G, Pow EHN, Tsoi JKH. Morphology and fracture behavior of lithium disilicate dental crowns designed by human and knowledge-based AI. J Mech Behav Biomed Mater. 2022;131:105256.
37. Cho JH, Yi Y, Choi J, Ahn J, Yoon HI, Yilmaz B. Time efficiency, occlusal morphology, and internal fit of anatomic contour crowns designed by dental software powered by generative adversarial network: a comparative study. J Dent. 2023;138:104739.
38. Teng TY, Wu JH, Lee CY. Acceptance and experience of digital dental technology, burnout, job satisfaction, and turnover intention for Taiwanese dental technicians. BMC Oral Health. 2022;22:342.
39. Litzenburger AP, Hickel R, Richter MJ, Mehl AC, Probst FA. Fully automatic CAD design of the occlusal morphology of partial crowns compared to dental technicians’ design. Clin Oral Invest. 2013;17:491–6.
40. 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 Oct;35(10):e3241.
41. Jreige CS, Kimura RN, Segundo ÂRTC, Coachman C, Sesma N. Esthetic treatment planning with digital animation of the smile dynamics: A technique to create a 4-dimensional virtual patient. J Prosthet Dent. 2022 Aug;128(2):130-138.
42. Pareek, M, Kaushik B. Artificial intelligence in prosthodontics: a scoping review on current applications and future possibilities. Int J Adv Med. 2022;9(3):367-70.
43. Alexander B, John S, Aralamoodu PO. Artificial intelligence in dentistry: Current concepts and a peep into the future. Int J Adv Res. 2008;6(12):1105-1108.
44. Larson TD. The clinical significance of marginal fit. Northwest Dent J. 2012;91:22.
45. Mai HN, Han JS, Kim HS, Park YS, Park JM, Lee DH. Reliability of automatic finish line detection for tooth preparation in dental computer aided software. J Prosthodont Res. 2023;67:138–43.
46. Choi J, Ahn J, Park JM. Deep learning-based automated detection of the dental crown finish line: an accuracy study. J Prosthet Dent. 2023;S0022–3913(23):00769–2.
47. Denli N, Uludağ B, Kılıçarslan MA, Özkan T. Resistance of artificial acrylic resin teeth to staining. Türkiye Klin Dişhek Bil Derg 1996; 2: 38-42.
48. Köksal T, Dikbas I. Color stability of different denture teeth materials against various staining agents. Dent Mater J 2008; 27: 139-144.
49. Imamura, S, Takahashi H, Hayakawa I, Loyaga-Rendon PG, Minakuchi S. Effect of filler type and polishing on the discoloration of composite resin artificial teeth. Dent Mater J 2008; 27: 802-808.
50. Kawano F, Ohguri T, Ichikawa T, Mizuno I, Hasegawa A. Shock absorbability and hardness of commercially available denture teeth. Int J Prosthodont 2002; 15: 243-247.
51. Zheng K, Liu HJ. Investigation on wear prediction model of dental restoration material based on ensemble learning. Mater Res Innov 2014; 18: 987-991.
52. Özkan G, Aliplik Akın B, Özkan G. The prediction of chemical oxygen demand (cod) or suspended solid (ss) removal using statistical methods and the artificial neural network in the sugar industrial wastewaters. J Eng Appl Sci 2013; 8: 978- 983.
53. Deniz ST, Ozkan P, Ozkan G. The accuracy of the prediction models for surface roughness and micro hardness of denture teeth. Dent Mater J. 2019 Dec 1;38(6):1012-1018.
54. Matin I, Hadzistevic M, Vukelic D, Potran M, Brajlih T. Development of an expert system for the simulation model for casting metal substructure of a metal-ceramic crown design. Comput Methods Programs Biomed. 2017 Jul;146:27-35.
55. Bauer FX, Gau D, Guell F, Eblenkamp M, Loeffelbein DJ. Automated detection of alveolar arches for nasoalveolar molding in cleft lip and palate treatment. Curr Dir Biomed Eng. 2016;2:701–5.
56. Ghods K, Azizi A, Jafari A, Ghods K. Application of Artificial Intelligence in Clinical Dentistry, a Comprehensive Review of Literature. J Dent (Shiraz). 2023 Dec 1;24(4):356-371.
57. Wei J, Peng M, Li Q, Wang Y. Evaluation of a Novel Computer Color Matching System Based on the Improved Back-Propagation Neural Network Model. J Prosthodont. 2018
58. Liu L, Zhou R, Yuan S, Sun Z, Lu X, Li J, et al. Simulation training for ceramic crown preparation in the dental setting using a virtual educational system. Eur J Dent Educ. 2020;24:199–206.
59. Shillingburg HT, Hobo S, Whitsett LD, Jacobi RBS. Fundamentals of fixed prosthodontics 3rd edition 1997.
60. Kateeb ET, Kamal MS, Kadamani AM, Abu Hantash RO, Abu Arqoub MM. Utilising an innovative digital software to grade pre-clinical crown preparation exercise. Eur J Dent Educ. 2017;21:220–7.
61. Feil PH, Gatti JJ. Validation of a motor skills performance theory with applications for dental education. J Dent Educ. 1993;57:628–33.
62. Han S, Yi Y, Revilla-León M, Yilmaz B, Yoon HI. Feasibility of softwarebased assessment for automated evaluation of tooth preparation for dental crown by using a computational geometric algorithm. Sci Rep. 2023;13:11847.
63. Schepke U, Meijer HJ, Vermeulen KM, Raghoebar GM, Cune MS. Clinical bonding of resin nano ceramic restorations to zirconia abutments: a case series within a randomized clinical trial. Clin Implant Dent Relat Res. 2016;18:984–92.
64. Rosentritt M, Preis V, Behr M, Krifka S. In-vitro performance of cad/cam crowns with insufficient preparation design. J Mech Behav Biomed Mater. 2019;90:269–74.
65. Yang Y, Yang Z, Zhou J, Chen L, Tan J. Effect of tooth preparation design on marginal adaptation of composite resin CAD-CAM onlays. J Prosthet Dent. 2020;124:88–93.
66. Yamaguchi S, Lee C, Karaer O, Ban S, Mine A, Imazato S. Predicting the debonding of cad/cam composite resin crowns with ai. J Dent Res. 2019;98:1234–8.
67. Shen KL, Huang CL, Lin YC, Du JK, Chen FL, Kabasawa Y, Chen CC, Huang HL. Effects of artificial intelligence-assisted dental monitoring intervention in patients with periodontitis: A randomized controlled trial. J Clin Periodontol. 2022 Oct;49(10):988-998.
68. Javaid M, Haleem A, Pratap Singh R, Suman R. Pedagogy and innovative care tenets in COVID-19 pandemic: An enhancive way through Dentistry 4.0. Sens Int. 2021;2:100118.
69. Lahoud P, Jacobs R, Boisse P, EzEldeen M, Ducret M, Richert R. Precision medicine using patient-specific modelling: state of the art and perspectives in dental practice. Clin Oral Investig. 2022 Aug;26(8):5117-5128.
70. Lee JH, Jeong SN. Efficacy of deep convolutional neural network algorithm for the identification and classification of dental implant systems, using panoramic and periapical radiographs: A pilot study. Medicine (Baltimore). 2020 Jun 26;99(26):e20787.
71. Ali IE, Tanikawa C, Chikai M, Ino S, Sumita Y, Wakabayashi N. Applications and performance of artificial intelligence models in removable prosthodontics: A literature review. J Prosthodont Res. 2024 Jul 8;68(3):358-367.
72. Ayodele TO. Types of machine learning algorithms. In: Zhang Y, editor. New Advances in Machine Learning, 2010, p. 19-48.
73. Chen Q, Lin S, Wu J, Lyu P, Zhou Y. Automatic drawing of customized removable partial denture diagrams based on textual design for the clinical decision support system. J Oral Sci. 2020;62:236–8.
74. Revilla-León M, Gómez-Polo M, Vyas S, Barmak AB, Gallucci GO, Att W, et al. Artificial intelligence models for tooth-supported fixed and removable prosthodontics: A systematic review. J Prosthet Dent. 2023;129:276–92.
75. Modgil S, Hutton TJ, Hammond P, Davenport JC. Combining biometric and symbolic models for customised, automated prosthesis design. Artif Intell Med. 2002;25:227–45.
76. Ariani N, Visser A, van Oort RP, et al.: Current state of craniofacial prosthetic rehabilitation . Int J Prosthodont. 2013, 26:57-67.
77. Susic I, Travar M, Susic M: The application of CAD/CAM technology in dentistry . IOP Conf Ser: Mater Sci Eng. 2017, 200:12-20.
78. Ciocca L, Mingucci R, Gassino G, Scotti R: CAD/CAM ear model and virtual construction of the mold. J Prosthet Dent. 2007, 98:339-43
79. Runte C, Dirksen D, Deleré H : Optical data acquisition for computer-assisted design of facial prostheses . Int J Prosthodont. 2002, 15:129-32.
80. Jiao T, Zhang F, Huang X, Wang C: Design and fabrication of auricular prostheses by CAD/CAM system . Int J Prosthodont. 2004, 17:460-3.
81. Kurt M, Kurt Z, Işık Ş. Using deep learning approaches for coloring silicone maxillofacial prostheses: A comparison of two approaches. J Indian Prosthodont Soc. 2023;23:84–9.
82. Jiao T, Zhang F, Huang X, Wang C. Design and fabrication of auricular prostheses by CAD/CAM system. Int J Prosthodont. 2004 Jul-Aug;17(4):460-3.
83. Kirubarajan A, Young D, Khan S, Crasto N, Sobel M, Sussman D. Artificial Intelligence and Surgical Education: A Systematic Scoping Review of Interventions. J Surg Educ. 2022 Mar-Apr;79(2):500-515.

Toplam 82 adet kaynakça vardır.

Ayrıntılar

Birincil Dil	İngilizce
Konular	Protez
Bölüm	Prosthodontics and Maxillofacial Prosthetics
Yazarlar	Bülent Kadir Tartuk 0000-0003-2282-8944
Yayımlanma Tarihi	30 Haziran 2025
Gönderilme Tarihi	5 Mart 2025
Kabul Tarihi	18 Mayıs 2025
Yayımlandığı Sayı	Yıl 2025 Cilt: 7 Sayı: 2

Kaynak Göster

Vancouver	Tartuk BK. The Role of Artificial Intelligence in Prosthetic Dentistry. Dent & Med J - R. 2025;7(2):71-87.

Kapak Resmi İndir

Makale Dosyaları

Tam Metin

"Dünyada herşey için, medeniyet için, hayat için, başarı için en gerçek yol gösterici ilimdir, fendir. İlim ve fennin dışında yol gösterici aramak gaflettir, cahilliktir, doğru yoldan sapmaktır. Yalnız ilmin ve fenin yaşadığımız her dakikadaki safhalarının gelişimini anlamak ve ilerlemeleri zamanında takip etmek şarttır. Bin, iki bin, binlerce yıl önceki ilim ve fen lisanının koyduğu kuralları, şu kadar bin yıl sonra bugün aynen uygulamaya kalkışmak elbette ilim ve fennin içinde bulunmak değildir."

M. Kemal ATATÜRK