Dijital Ortodonti ve Yapay Zekâ Uygulamaları
Özet
Dijital model analizi ve CAD/CAM sistemleri, ortodontik prosedürleri kolaylaştırarak verimliliği artırmakta, dijital sefalometri ve otomatik değerlendirme araçları da tanı ve tedavi planlama hassasiyetini kolaylaştırmaktadır. Üç boyutlu üretim, kişiye özel ortodontik apareylerin oluşturulmasını kolaylaştırarak hastalara gelişmiş konfor ve uyum sağlamaktadır. Yapay zekâ, tedavi planlamasında devrim yaparak ortodontistlere sonuç tahmini ve strateji optimizasyonu için güçlü araçlar sunmaya hazırlanmaktadır. Mobil uygulamalar ve Nesnelerin İnterneti (IoT) cihazları, uzaktan hasta takibi için yeni yollar sunarak hızlı müdahalelere ve tedaviye uyumun iyileştirilmesine imkân tanımaktadır. Dijital teknolojilerin ve yapay zekânın sürekli gelişimi, ortodontiyi geliştirmede muazzam bir potansiyele sahip olup, üstün tedavi sonuçları ve artan hasta memnuniyeti sağlamaktadır. Yapay zekânın ortodontiye entegrasyonu, klinik pratiğin çeşitli yönlerini dönüştürme potansiyeline sahip, hızla gelişen bir alandır.
Digital model analysis and CAD/CAM systems simplify orthodontic procedures, increasing efficiency, while digital cephalometry and automated assessment tools enhance diagnostic and treatment planning accuracy. Three-dimensional fabrication facilitates the creation of custom orthodontic appliances, providing patients with enhanced comfort and compliance. Artificial intelligence is poised to revolutionize treatment planning, providing orthodontists with powerful tools for outcome prediction and strategy optimization. Mobile applications and Internet of Things (IoT) devices offer new ways to monitor patients remotely, enabling rapid interventions and improving treatment adherence. The continued development of digital technologies and artificial intelligence holds tremendous potential to advance orthodontics, leading to superior treatment outcomes and increased patient satisfaction. The integration of artificial intelligence into orthodontics is a rapidly evolving field with the potential to transform various aspects of clinical practice.
Referanslar
Tarraf NE, Ali DM. Present and the future of digital orthodontics✰. Seminars in Orthodontics; 2018;24(4), 376–385. doi:10.1053/j.sodo.2018.10.002
Alauddin MS, Baharuddin AS, Mohd Ghazali MI. The Modern and Digital Transformation of Oral Health Care: A Mini Review. Healthcare (Basel); 2021;9(2):118. doi:10.3390/healthcare9020118
Pandey R, Kamble R, Kanani H. Revolutionizing Smiles: Advancing Orthodontics Through Digital Innovation. Cureus; 2024;16(7):e64086. doi:10.7759/cureus.64086
Tallarico M. Computerization and Digital Workflow in Medicine: Focus on Digital Dentistry. Materials (Basel); 2020;13(9):2172. doi:10.3390/ma13092172
Joda T, Yeung AWK, Hung K, et al. Disruptive Innovation in Dentistry: What It Is and What Could Be Next. Journal of Dental Research; 2021;100(5):448–453. doi:10.1177/0022034520978774
Dhopte A, Bagde H. Smart Smile: Revolutionizing Dentistry with Artificial Intelligence. Cureus; 2023;15(6):e41227. doi: 10.7759/cureus.41227
Shelke DD, Jadhav VV, Jaiswal A, et al. Review on Digital Orthodontics. Journal of Pharmaceutical Research International; 2022;34:16–26. doi:10.9734/jpri/2022/v34i2b35377
Thurzo A, Kočiš F, Novák B, et al. Three-Dimensional Modeling and 3D Printing of Biocompatible Orthodontic Power-Arm Design with Clinical Application. Applied Sciences; 2021;11(20), 9693. doi:10.3390/app11209693
Tomita Y, Uechi J, Konno M, et al. Accuracy of digital models generated by conventional impression/plaster-model methods and intraoral scanning. Dental Materials Journal; 2018;37(4):628–633. doi: 10.4012/dmj.2017-208
Tordiglione L, De Franco M, Bosetti G. The Prosthetic Workflow in the Digital Era. International Journal of Dentistry; 2016;2016:9823025. doi: 10.1155/2016/9823025.
Vij AA, Reddy A. Using digital impressions to fabricate space maintainers: A case report. Clinical Case Report; 2020;8(7):1274-1276. doi: 10.1002/ccr3.2848
Kihara H, Hatakeyama W, Komine F, et al. Accuracy and practicality of intraoral scanner in dentistry: A literature review. Journal of Prosthodontic Research; 2020;64(2):109-113. doi: 10.1016/j.jpor.2019.07.010.
Sacher M, Schulz GV, Deyhle H, et al. Accuracy of commercial intraoral scanners. Journal of Medical Imaging; 2021;8(3). https://doi.org/10.1117/1.jmi.8.3.035501
da Silva Salomão GV, Chun EP, Panegaci RDS, et al. Analysis of Digital Workflow in Implantology. Case Reports in Dentistry; 2021;2021:6655908. doi: 10.1155/2021/6655908
Abad‐Coronel C, Peña Aguilar AJ. Chairside Digital Workflow in Restorative Dentistry. Biomedical Journal of Scientific & Technical Research; 2019;13(4), 1–3. https://doi.org/10.26717/bjstr.2019.13.002431
Alqarni H, AlHelal A, Kattadiyil MT, et al. Selective implant scan body modification to restore severely tilted adjacent implants: A completely digital workflow. Journal of Prosthetic Dentistry; 2020;123(1):38–41. doi: 10.1016/j.prosdent.2019.04.012.
Nguyen T, Jackson TH. 3D technologies for precision in orthodontics. Seminars in Orthodontics; 2018;24(4), 386–392. doi:10.1053/j.sodo.2018.10.003
Tango RN, Todorović A, Stamenković D, et al. Effect of Staining and Aging on Translucency Parameter of CAD-CAM Materials. Acta Stomatologica Croatica; 2021;55(1):2–9. doi: 10.15644/asc55/1/1
Tamer İ, Öztaş E, Marşan G. Orthodontic Treatment with Clear Aligners and The Scientific Reality Behind Their Marketing: A Literature Review. Turkish Journal of Orthodontics; 2019;32(4):241–246. doi: 10.5152/TurkJOrthod.2019.18083
Umapathy VR, Rajinikanth B S, Samuel Raj RD, et al. Perspective of Artificial Intelligence in Disease Diagnosis: A Review of Current and Future Endeavours in the Medical Field. Cureus; 2023;15(9):e45684. doi: 10.7759/cureus.45684
Shah, A. Implications of CBCT in Dentistry: A Review. Medical & Clinical Reviews; 2017;3(3), 1–4. https://doi.org/10.21767/2471-299x.1000057
Gonca M, Bayrakdar İŞ, Çelik Ö. Does the FARNet neural network algorithm accurately identify Posteroanterior cephalometric landmarks? BMC Medical Imaging; 2024;24(1):294. doi:10.1186/s12880-024-01478-z
Koz S, Uslu-Akcam O. Artificial Intelligence-Supported and App-Aided Cephalometric Analysis: Which One Can We Trust? Diagnostics (Basel); 2025;15(5):559. doi:10.3390/diagnostics15050559.
Vranckx M, Van Gerven A, Willems H, et al. Artificial Intelligence (AI)-Driven Molar Angulation Measurements to Predict Third Molar Eruption on Panoramic Radiographs.International Journal of Environmental Research and Public Health; 2020;17(10):3716. doi: 10.3390/ijerph17103716
Shan T, Tay FR, Gu L. Application of Artificial Intelligence in Dentistry. Journal of Dental Research; 2021;100(3), 232–244. https://doi.org/10.1177/0022034520969115
Kim H, Kim CS, et al. Prediction of Fishman's skeletal maturity indicators using artificial intelligence. Scientific Reports; 2023;13(1):5870. doi: 10.1038/s41598-023-33058-6
Orhan K, Shamshiev M, Ezhov M, et al. AI-based automatic segmentation of craniomaxillofacial anatomy from CBCT scans for automatic detection of pharyngeal airway evaluations in OSA patients. Scientific Reports; 2022;12(1):11863. doi:10.1038/s41598-022-15920-1
Jain S, Choudhary K, Nagi R, et al. New evolution of cone-beam computed tomography in dentistry: Combining digital technologies. Imaging Science in Dentistry; 2019;49(3):179–190. doi: 10.5624/isd.2019.49.3.179.
Sarwar S, Jabin S. AI Techniques for Cone Beam Computed Tomography in Dentistry: Trends and Practices. arXiv (Cornell University). 2023, doi:10.48550/arxiv.2306.03025
Çokakoğlu S, Karaokutan I, Buyuk SK. Effects of orientation and postprocessing on the bonding of 3-dimensional printed brackets to printable permanent crown resin. American Journal of Orthodics and Dentofacial Orthopedics; 2025;167(3):362–370. doi:10.1016/j.ajodo.2024.11.009.
Korkmaz YN, Buyuk SK, Simsek H, et al. Comparison of the Flexural Strength of Three Different Aged and Nonaged 3D-Printed Permanent Crown Resins. International Journal of Prosthodontics; 2024;37(7):203–207. doi: 10.11607/ijp.8987
Demirsoy KK, Buyuk SK, Abay F, et al. A comparison of bond strength and adhesive remnant index of 3D-printed and metal orthodontic brackets attached using different adhesives. Australasian Orthodontic Journal; 2023; 39(2):32–39. doi: 10.2478/aoj-2023-0024
Della Bona A, Cantelli V, Britto VT, et al. 3D printing restorative materials using a stereolithographic technique: a systematic review. Dental Materials; 2021;37(2):336–350. doi: 10.1016/j.dental.2020.11.030
Dawood A, Marti BM, Sauret-Jackson V, et al. (2015). 3D printing in dentistry. British Dental Journal; 2015; 219(11):521–529. doi:10.1038/sj.bdj.2015.914
Derban P, Negrea R, Rominu M, et al. Influence of the Printing Angle and Load Direction on Flexure Strength in 3D Printed Materials for Provisional Dental Restorations. Materials; 2021;14(12):3376. doi: 10.3390/ma14123376
Pawar BA. Maintenance of space by innovative three-dimensional-printed band and loop space maintainer. Journal of Indian Society of Pedodontics and Preventive Dentistry; 2019;37(2):205–208. doi: 10.4103/JISPPD.JISPPD_9_19
Goracci C, Juloski J, D'Amico C, et al. Clinically Relevant Properties of 3D Printable Materials for Intraoral Use in Orthodontics: A Critical Review of the Literature. Materials (Basel); 2023;16(6):2166. doi: 10.3390/ma16062166
Meglioli M, Naveau A, Macaluso GM, et al. 3D printed bone models in oral and cranio-maxillofacial surgery: a systematic review. 3D Printing in Medicine; 2020;6(1):30. doi: 10.1186/s41205-020-00082-5. Erratum in: 3D Printing in Medicine; 2020;6(1):36. doi: 10.1186/s41205-020-00088-z
Park GS, Kim SK, Heo SJ, et al. Effects of Printing Parameters on the Fit of Implant-Supported 3D Printing Resin Prosthetics. Materials (Basel); 201;12(16):2533. doi: 10.3390/ma12162533
Boreak N. Effectiveness of Artificial Intelligence Applications Designed for Endodontic Diagnosis, Decision-making, and Prediction of Prognosis: A Systematic Review. The Journal of Contemporary Dental Practice; 2020;21(8):926–934. doi:10.5005/jp-journals-10024-2894
Mayta-Tovalino F, Munive-Degregori A, Luza S, et al. Applications and Perspectives of Artificial Intelligence, Machine Learning and "Dentronics" in Dentistry: A Literature Review. Journal of International Society of Preventive and Community Dentistry. 2023;13(1):1–8. doi: 10.4103/jispcd.JISPCD_35_22
Serindere G, Bilgili E, Yesil C, et al. Evaluation of maxillary sinusitis from panoramic radiographs and cone-beam computed tomographic images using a convolutional neural network. Imaging Science in Dentistry. 2022;52(2):187–195. doi: 10.5624/isd.20210263
Ding H, Wu J, Zhao W, et al. Artificial intelligence in dentistry—A review. Frontiers in Dental Medicine; 2023, 4, 1085251, doi:10.3389/fdmed.2023.1085251
Ozsunkar PS, Özen DÇ, Abdelkarim AZ, et al. Detecting white spot lesions on post-orthodontic oral photographs using deep learning based on the YOLOv5x algorithm: a pilot study. BMC Oral Health; 2024;24(1):490. doi: 10.1186/s12903-024-04262-1
Gao S, Wang X, Xia Z, et al. Artificial Intelligence in Dentistry: A Narrative Review of Diagnostic and Therapeutic Applications. Medical Science Monitor; 2025;31:e946676. doi: 10.12659/MSM.946676
Schwendicke F, Samek W, Krois J. Artificial Intelligence in Dentistry: Chances and Challenges. Journal of Dental Research; 2020;99(7):769–774. doi: 10.1177/0022034520915714
Sharma S, Kumari P, Sabira K, et al. Revolutionizing Dentistry: The Applications of Artificial Intelligence in Dental Health Care. Journal of Pharmacy and Bioallied Sciences; 2024;16(Suppl 3):S1910–S1912. doi: 10.4103/jpbs.jpbs_1290_23
Charow R, Jeyakumar T, Younus S, et al. Artificial Intelligence Education Programs for Health Care Professionals: Scoping Review. JMIR Medical Education; 2021;7(4):e31043. doi: 10.2196/31043.
Briganti G, Le Moine O. Artificial Intelligence in Medicine: Today and Tomorrow. Frontiers in Medicine; 2020;7:27. doi: 10.3389/fmed.2020.00027
Chang, A. The Role of Artificial Intelligence in Digital Health. In Computers in health care (p. 71). Springer International Publishing. 2019, doi: 10.1007/978-3-030-12719-0_7
Fouad H, Hassanein AS, Soliman AM, et al. Analyzing patient health information based on IoT sensor with AI for improving patient assistance in the future direction. Measurement; 2020;159, 107757. doi: 10.1016/j.measurement.2020.107757
Al-Kahtani MS, Khan F, Taekeun W. Application of Internet of Things and Sensors in Healthcare. Sensors (Basel); 2022;22(15):5738. doi: 10.3390/s22155738
Ziwei H, Dongni Z, Man Z, et al. The applications of internet of things in smart healthcare sectors: a bibliometric and deep study. Heliyon; 2024;10(3):e25392. doi: 10.1016/j.heliyon.2024.e25392
Meisami S, Meisami S, Yousefi M. Combining Blockchain and IoT for Decentralized Healthcare Data Management. International Journal on Cryptography and Information Security; 2023;13(1), 35. https://doi.org/10.5121/ijcis.2023.13102
Thacharodi A, Singh P, Meenatchi R, et al. Revolutionizing healthcare and medicine: The impact of modern technologies for a healthier future-A comprehensive review. Health Care Science; 2024;3(5):329–349. doi: 10.1002/hcs2.115
