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YAPAY ZEKÂ PROTETİK DİŞ TEDAVİSİNDE KLİNİSYENLERİN YERİNİ ALABİLİR Mİ?

Yıl 2025, Cilt: 34 Sayı: 1, 128 - 135
https://doi.org/10.34108/eujhs.1508246

Öz

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.

Etik Beyan

Sağlık Bilimleri Dergisi’nde yayımlanmak üzere değerlendirilmesi talebiyle sisteme yüklediğimiz “Yapay Zekâ Protetik Diş Tedavisinde Klinisyenlerin Yerini Alabilir Mi?” başlıklı makalemiz derleme kapsamında olduğundan Etik Kurul onayına ihtiyaç yoktur.

Destekleyen Kurum

Yoktur

Proje Numarası

Yoktur

Teşekkür

Yoktur

Kaynakça

  • 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/
  • 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
  • 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
  • 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
  • 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
  • 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
  • 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
  • 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
  • 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
  • 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
  • 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
  • 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
  • Nordblom NF, Büttner M, Schwendicke F. Artificial intelligence in orthodontics: Critical review. J Dent Res. 2024;103(6):577-584. doi:10.1177/00220345241235606
  • 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
  • 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
  • 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
  • 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
  • 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
  • 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
  • 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
  • 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
  • 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
  • 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
  • Yüce F, Taşsöker M. Diş hekimliğinde yapay zeka uygulamaları. 7 tepe Klinik Dergisi. 2023;19(2):141-149.doi:10.5505/yeditepe.2023.05668
  • 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
  • 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
  • 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
  • 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
  • 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
  • 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
  • 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
  • Ceylan G, Emir F. Estetik ve protetik yapay zekâ uygulamalarında güncel ve gelecek vadeden yaklaşımlar. Turkiye Klinikleri J DentalSci. 2023:38-44.
  • 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
  • Koçak Topbaş N. Diş hekimliği alanında uluslararası teori, araştırma ve derlemeler. 1. Basım, Serüven Yayınevi, c2023: 99-110. ISBN: 978-625-6760-23-3
  • 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
  • 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
  • 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
  • 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
  • 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
  • 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
  • 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
  • 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
  • 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
  • Agrawal P, Nikhade P. Artificial intelligence in dentistry: past, present, and future. Cureus. 2022;14(7):e27405.doi:10.7759/cureus.27405

CAN ARTIFICIAL INTELLIGENCE REPLACE CLINICIANS IN PROSTHETIC DENTISTRY?

Yıl 2025, Cilt: 34 Sayı: 1, 128 - 135
https://doi.org/10.34108/eujhs.1508246

Öz

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.

Etik Beyan

Since our article entitled with "Can Artificial Intelligence Replace Clinicians in Prosthetic Dentistry?", which we uploaded to the system with the request to be evaluated for publication in the Journal of Health Sciences is within the scope of the review, Ethics Committee approval is not required.

Destekleyen Kurum

None

Proje Numarası

Yoktur

Teşekkür

None

Kaynakça

  • 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/
  • 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
  • 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
  • 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
  • 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
  • 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
  • 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
  • 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
  • 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
  • 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
  • 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
  • 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
  • Nordblom NF, Büttner M, Schwendicke F. Artificial intelligence in orthodontics: Critical review. J Dent Res. 2024;103(6):577-584. doi:10.1177/00220345241235606
  • 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
  • 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
  • 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
  • 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
  • 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
  • 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
  • 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
  • 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
  • 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
  • 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
  • Yüce F, Taşsöker M. Diş hekimliğinde yapay zeka uygulamaları. 7 tepe Klinik Dergisi. 2023;19(2):141-149.doi:10.5505/yeditepe.2023.05668
  • 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
  • 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
  • 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
  • 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
  • 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
  • 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
  • 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
  • Ceylan G, Emir F. Estetik ve protetik yapay zekâ uygulamalarında güncel ve gelecek vadeden yaklaşımlar. Turkiye Klinikleri J DentalSci. 2023:38-44.
  • 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
  • Koçak Topbaş N. Diş hekimliği alanında uluslararası teori, araştırma ve derlemeler. 1. Basım, Serüven Yayınevi, c2023: 99-110. ISBN: 978-625-6760-23-3
  • 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
  • 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
  • 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
  • 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
  • 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
  • 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
  • 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
  • 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
  • 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
  • Agrawal P, Nikhade P. Artificial intelligence in dentistry: past, present, and future. Cureus. 2022;14(7):e27405.doi:10.7759/cureus.27405
Toplam 44 adet kaynakça vardır.

Ayrıntılar

Birincil Dil Türkçe
Konular Protez
Bölüm Derleme
Yazarlar

Zühre Aşıcıoğlu 0009-0001-0924-4031

Ferhan Egilmez 0000-0001-9325-8761

Proje Numarası Yoktur
Erken Görünüm Tarihi 17 Mart 2025
Yayımlanma Tarihi
Gönderilme Tarihi 1 Temmuz 2024
Kabul Tarihi 1 Kasım 2024
Yayımlandığı Sayı Yıl 2025 Cilt: 34 Sayı: 1

Kaynak Göster

APA Aşıcıoğlu, Z., & Egilmez, F. (2025). YAPAY ZEKÂ PROTETİK DİŞ TEDAVİSİNDE KLİNİSYENLERİN YERİNİ ALABİLİR Mİ?. Sağlık Bilimleri Dergisi, 34(1), 128-135. https://doi.org/10.34108/eujhs.1508246
AMA Aşıcıoğlu Z, Egilmez F. YAPAY ZEKÂ PROTETİK DİŞ TEDAVİSİNDE KLİNİSYENLERİN YERİNİ ALABİLİR Mİ?. JHS. Mart 2025;34(1):128-135. doi:10.34108/eujhs.1508246
Chicago Aşıcıoğlu, Zühre, ve Ferhan Egilmez. “YAPAY ZEKÂ PROTETİK DİŞ TEDAVİSİNDE KLİNİSYENLERİN YERİNİ ALABİLİR Mİ?”. Sağlık Bilimleri Dergisi 34, sy. 1 (Mart 2025): 128-35. https://doi.org/10.34108/eujhs.1508246.
EndNote Aşıcıoğlu Z, Egilmez F (01 Mart 2025) YAPAY ZEKÂ PROTETİK DİŞ TEDAVİSİNDE KLİNİSYENLERİN YERİNİ ALABİLİR Mİ?. Sağlık Bilimleri Dergisi 34 1 128–135.
IEEE Z. Aşıcıoğlu ve F. Egilmez, “YAPAY ZEKÂ PROTETİK DİŞ TEDAVİSİNDE KLİNİSYENLERİN YERİNİ ALABİLİR Mİ?”, JHS, c. 34, sy. 1, ss. 128–135, 2025, doi: 10.34108/eujhs.1508246.
ISNAD Aşıcıoğlu, Zühre - Egilmez, Ferhan. “YAPAY ZEKÂ PROTETİK DİŞ TEDAVİSİNDE KLİNİSYENLERİN YERİNİ ALABİLİR Mİ?”. Sağlık Bilimleri Dergisi 34/1 (Mart 2025), 128-135. https://doi.org/10.34108/eujhs.1508246.
JAMA Aşıcıoğlu Z, Egilmez F. YAPAY ZEKÂ PROTETİK DİŞ TEDAVİSİNDE KLİNİSYENLERİN YERİNİ ALABİLİR Mİ?. JHS. 2025;34:128–135.
MLA Aşıcıoğlu, Zühre ve Ferhan Egilmez. “YAPAY ZEKÂ PROTETİK DİŞ TEDAVİSİNDE KLİNİSYENLERİN YERİNİ ALABİLİR Mİ?”. Sağlık Bilimleri Dergisi, c. 34, sy. 1, 2025, ss. 128-35, doi:10.34108/eujhs.1508246.
Vancouver Aşıcıoğlu Z, Egilmez F. YAPAY ZEKÂ PROTETİK DİŞ TEDAVİSİNDE KLİNİSYENLERİN YERİNİ ALABİLİR Mİ?. JHS. 2025;34(1):128-35.