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.
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
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?
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.
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
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