Diş Hekimliğinde Yeni Ufuklar: Minimal İnvaziv Yaklaşımlar, Yapay Zekâ ve Sanal Gerçeklik

Cesur Aydın K. Ağız, Diş ve Çene Radyolojisinde Yapay Zekâ Uygulamaları Neler Yapabiliyor? Turkiye Klinikleri Oral and Maxillofacial Radiology-Special Topics. 2023;9(1): 9-15.

Schwendicke F, Golla T, Dreher M, et al. Convolutional neural networks for dental image diagnostics: A scoping review. Journal of Dentistry. 2019;91: 103226. https://doi.org/10.1016/j.jdent.2019.103226

Desai H, Stewart CA, Finer Y. Minimally invasive therapies for the management of dental caries—A literature review. Dentistry Journal. 2021;9(12): 147. https://doi.org/10.3390/dj9120147

Frencken JE, Peters MC, Manton DJ, et al. Minimal intervention dentistry for managing dental caries – a review. International Dental Journal. 2012;62(2): 3–14. https://doi.org/10.1111/idj.12007

Semerci ZM, Yardımcı S. Empowering Modern Dentistry: The Impact of Artificial Intelligence on Patient Care and Clinical Decision Making. Diagnostics.2024;14(12):1260. https://doi.org/10.3390/diagnostics14121260

Pottle J. Virtual reality and the transformation of medical education. Future Healthcare Journal. 2019;6(3):181–185. https://doi.org/10.7861/fhj.2019-0036

Farag A, Hashem D. Impact of the haptic virtual reality simulator on dental students’ psychomotor skills in preclinical operative dentistry. Clinics and Practice. 2022;12(1):17–26. https://doi.org/10.3390/clinpract12010003

Alam BF, Najmi MA, Qasim SB, et al. A bibliometric analysis of minimally invasive dentistry: A review of the literature from 1994 to 2021. Research and Education. 2021:1–2. https://doi.org/10.1016/j.prosdent.2021.09.023

Featherstone JD. The science and practice of caries prevention. Journal of the American Dental Association. 2000;131(7):887–899. https://doi.org/10.14219/jada.archive.2000.0307

Baccolini V, et al. The Role of Casein Phosphopeptide-Amorphous Calcium Phosphate (CPP-ACP) in White Spot Lesion Remineralization—A Systematic Review. Journal of Functional Biomaterials. 2025;16(8):272. https://doi.org/10.3390/jfb16080272

Pushpalatha C, Gayathri VS, Sowmya SV, et al. Nanohydroxyapatite in dentistry: A comprehensive review. The Saudi Dental Journal. 2023;35(6):741-752. https://doi.org/10.1016/j.sdentj.2023.05.018

Sleem MM, Eid ESG, Abdelghany AM, et al. Evaluation of the remineralization potential of different bioactive glass varnishes on white spot lesions: an in vitro study. BMC Oral Health. 2025;25(1):1284. https://doi.org/10.1186/s12903-025-06665-0

Tyagi G, Jain S, Deshwal S, et al. Comparative study of dentin remineralization with Nano-amorphous calcium phosphate-modified bioactive restoratives. Journal of Oral Biology and Craniofacial Research. 2025;15(4):684-690. https://doi.org/10.1016/j.jobcr.2025.04.009

Alkilzy M, Tarabaih A, Santamaria RM, et al. Self-assembling peptide P11-4 and fluoride for regenerating enamel. Journal of Dental Research. 2018;97(2):148-154. https://doi.org/10.1177/0022034517730531

Naim J, Sen S. The Remineralizing and Desensitizing Potential of Hydroxyapatite in Dentistry: A Narrative Review of Recent Clinical Evidence. Journal of Functional Biomaterials. 2025;16(9):325. https://www.mdpi.com/2079-4983/16/9/325#

Tyas MJ, Anusavice KJ, Frencken JE, et al. Minimal intervention dentistry—a review. FDI Commission Project 1e97. International Dental Journal. 2000;50(1):1–2. https://doi.org/10.1111/j.1875-595x.2000.tb00540.x

Arrow P, McPhee R, Atkinson D, et al. Minimally invasive dentistry based on atraumatic restorative treatment to manage early childhood caries in rural and remote aboriginal communities: Protocol for a randomized controlled trial. JMIR Research Protocols. 2018;7(7): e10322. https://doi.org/10.2196/10322

Kielbassa AM, Muller J, Gernhardt CR. Closing the gap between oral hygiene and minimally invasive dentistry: A review on the resin infiltration technique of incipient (proximal) enamel lesions. Quintessence International. 2009;40(8):663–681.

Silva EJNL, Martins JNR, Resende BF. Ten years of minimally invasive access cavities in endodontics: A bibliometric analysis. Restorative Dentistry & Endodontics. 2021;46(e42):1–15. https://doi.org/10.5395/rde.2021.46.e42

Alsolamy M, Nadeem F, Azhari AA, et al. Automated detection and labeling of posterior teeth in dental bitewing X-rays using deep learning. Computers in Biology and Medicine. 2024; 183:109262. https://doi.org/10.1016/j.compbiomed.2024.109262

Ari T, Sağlam H, Öksüzoğlu H, et al. Automatic feature segmentation in dental periapical radiographs. Diagnostics. 2022;12(12):3081. https://doi.org/10.3390/diagnostics12123081

Bonfanti-Gris M, Herrera A, Salido Rodríguez-Manzaneque MP, et al. Deep learning for tooth detection and segmentation in panoramic radiographs: a systematic review and meta-analysis. BMC Oral Health. 2025;25(1):1280. https://doi.org/10.1186/s12903-025-06349-9

Miki Y, Muramatsu C, Hayashi T, et al. Tooth labeling in cone-beam CT using deep convolutional neural network for forensic identification. In: Medical Imaging 2017: Computer-Aided Diagnosis (Vol. 10134). 11-16 Şubat 2017, Orlando, Florida, USA: SPIE; 2017. p. 874-879. http://dx.doi.org/10.1117/12.2254332

Lee JH, Kim DH, Jeong SN, et al. Detection and diagnosis of dental caries using a deep learning-based convolutional neural network algorithm. Journal of Dentistry. 2018; 77:106-111. https://doi.org/10.1016/j.jdent.2018.07.015

Casalegno F, Newton T, Daher R, et al. Caries detection with near-infrared transillumination using deep learning. Journal of Dental Research. 2019;98(11):1227–1233. https://doi.org/10.1177/0022034519871884

Hung M, Voss MW, Rosales MN, et al. Application of machine learning for diagnostic prediction of root caries. Gerodontology. 2019;36(4):395–404. https://doi.org/10.1111/ger.12432

Musri N, Christie B, Ichwan SJA, et al. Deep learning convolutional neural network algorithms for the early detection and diagnosis of dental caries on periapical radiographs: A systematic review. Imaging Science in Dentistry. 2021;51(3):237. https://doi.org/10.5624/isd.20210074

Lee S, Oh SI, Jo J, et al. Deep learning for early dental caries detection in bitewing radiographs. Scientific Reports. 2021;11(1):16807. https://doi.org/10.1038/s41598-021-96368-7

Tan R, Zhu X, Chen S, et al. Caries lesions diagnosis with deep convolutional neural network in intraoral QLF images by handheld device. BMC Oral Health. 2024;24(1):754. https://doi.org/10.1186/s12903-024-04517-x

Bahammam, S. A. Prediction of dental caries in children through machine learning. Journal of Clinical Pediatric Dentistry. 2025;49(5):158-167. https://www.jocpd.com/articles/10.22514/jocpd.2025.110

Murata M, Ariji Y, Ohashi Y, et al. Deep-learning classification using convolutional neural network for evaluation of maxillary sinusitis on panoramic radiography. Oral Radiology. 2019;35(3):301–307. https://doi.org/10.1007/s11282-018-0363-7

Lee SJ, Chung D, Asano A, et al. Diagnosis of tooth prognosis using artificial intelligence. Diagnostics. 2022;12(6):1422. https://doi.org/10.3390/diagnostics12061422

Khanagar SB, Al-Ehaideb A, Vishwanathaiah S. Scope and performance of artificial intelligence technology in orthodontic diagnosis, treatment planning, and clinical decision-making - A systematic review. Journal of Dental Sciences. 2021;16(1):482–492. https://doi.org/10.1016/j.jds.2020.05.022

Hung K, Montalvao C, Tanaka R, et al. The use and performance of artificial intelligence applications in dental and maxillofacial radiology: A systematic review. Dentomaxillofacial Radiology. 2020;49(1):20190107. https://doi.org/10.1259/dmfr.20190107

Challen R, Denny J, Pitt M, et al. Artificial intelligence, bias and clinical safety. BMJ Quality & Safety. 2019;28(3):231–237. https://doi.org/10.1136/bmjqs-2018-008370

Schoenherr JR, Abbas R, Michael K, et al. Designing AI using a human-centered approach: Explainability and accuracy toward trustworthiness. IEEE Transactions on Technology and Society. 2023;4(1):9–23. https://doi.org/10.1109/TTS.2023.3257627

Kim J, Cai ZR, Chen ML, et al. Assessing Biases in Medical Decisions via Clinician and AI Chatbot Responses to Patient Vignettes. JAMA Network Open. 2023;6(10): e2338050–e2338050. https://doi.org/10.1001/jamanetworkopen.2023.38050

Gurusamy K, Aggarwal R, Palanivelu L, et al. Virtual reality training for surgical trainees in laparoscopic surgery. Cochrane Database of Systematic Reviews. 2009;(1):CD006575. https://doi.org/10.1002/14651858.cd006575.pub2

Towers AC. Deconstructed Learning in Pre-Clinical Dental Education Using Virtual Reality Simulation [Doktora tezi]. White Rose eTheses Online; 2023.

Wang D, Zhao X, Shi Y, et al. Six degree-of-freedom haptic simulation of probing dental caries within a narrow oral cavity. IEEE Transactions on Haptics. 2016;9(2):279–291. https://doi.org/10.1109/toh.2016.2531660

Hadjichristou C, Kokoti M, Bakopoulou A. Haptics in fixed prosthodontics and their role in dental education: A literature review. Journal of Dental Education. 2024;88(8):1020–1028. https://doi.org/10.1002/jdd.13533

Moussa R, Alghazaly A, Althagafi N, et al. Effectiveness of Virtual Reality and Interactive Simulators on Dental Education Outcomes: Systematic Review. European Journal of Dentistry. 2022;16(3): e42. https://doi.org/10.1055/s-0041-1731837

Norman G, Dore K, Grierson L. The minimal relationship between simulation fidelity and transfer of learning. Medical Education. 2012;46(7):636–647. https://doi.org/10.1111/j.1365-2923.2012.04243.x

Schoenherr JR, Hamstra SJ. Beyond fidelity: Deconstructing the seductive simplicity of fidelity in simulator-based education in the health care professions. Simulation in Healthcare. 2017;12(2):117–123. https://doi.org/10.1097/sih.0000000000000226

Massoth C, Röder H, Ohlenburg H, et al. High-fidelity is not superior to low-fidelity simulation but leads to overconfidence in medical students. BMC Medical Education. 2019;19(1):1–8. https://doi.org/10.1186/s12909-019-1464-7

Salari N, Ghasemnezhad F, Almasi-Hashiani A, et al. The Impact of Different Teaching Methods on Clinical Reasoning and Clinical. Journal of Dental Education. 2025; 0:1–15. https://doi.org/10.1002/jdd.13930

Koufidis C, Manninen K, Nieminen J, et al. Grounding judgement in context: A conceptual learning model of clinical reasoning. Medical Education. 2020;54(11):1019–1028. https://doi.org/10.1111/medu.14222

Gardner AK, Abdelfattah K, Wiersch J, et al. Embracing errors in simulation-based training: The effect of error training on retention and transfer of central venous catheter skills. Journal of Surgical Education. 2015;72(6): e158–e162. https://doi.org/10.1016/j.jsurg.2015.08.002

Uribe SE, Maldupa I, Schwendicke F. Integrating Generative AI in Dental Education: A Scoping Review of Current Practices and Recommendations. European Journal of Dental Education. 2025:1–15. https://doi.org/10.1111/eje.13074

Cutrer WB, Miller B, Pusic MV, et al. Fostering the development of master adaptive learners: a conceptual model to guide skill acquisition in medical education. Academic Medicine. 2017;92(1):70-75. https://doi.org/10.1097/acm.0000000000001323