Modern Endodontide Yapay Zeka Tabanlı Karar Destek Sistemleri
Özet
Bu çalışma, modern endodontide Yapay Zeka (YZ), Makine Öğrenmesi (ML) ve Derin Öğrenme (DL) teknolojilerinin entegrasyonunu ve klinik pratik üzerindeki etkilerini kapsamlı bir şekilde incelemektedir. Geleneksel endodonti pratiğinde hekimler arası tanı uyumsuzluğu önemli bir sorun teşkil ederken; YZ tabanlı sistemler, dijital radyografi ve KIBT verilerini işleyerek bu süreci daha objektif ve standart bir hale getirmektedir. Özellikle Konvolüsyonel Sinir Ağları (CNN), periapikal lezyonların tespiti, kök kanal morfolojisinin analizi ve dikey kök kırıklarının teşhisinde %90'ın üzerinde doğruluk oranları sergileyerek klinisyenlere uzman düzeyinde karar destek mekanizması sunmaktadır. YZ; çalışma boyu ölçümü, tedavi prognozunun öngörülmesi ve indirekt restorasyonların takibi gibi kritik aşamalarda da yüksek hassasiyetle çalışmaktadır. Teknolojinin sunduğu bu avantajlara rağmen; veri güvenliği, algoritmik şeffaflık (açıklanabilir YZ) ve tıbbi sorumluluk gibi etik ve yasal zorluklar endodontiye tam entegrasyon önünde engel teşkil etmektedir. Gelecekte robotik sistemler ve moleküler biyo-belirteç analiziyle birleşecek olan YZ, "Hassas Endodonti" dönemini başlatarak tedavi başarısını daha öngörülebilir kılacaktır.
This study provides a comprehensive analysis of the integration of Artificial Intelligence (AI), Machine Learning (ML), and Deep Learning (DL) technologies in modern endodontics and their impact on clinical practice. While inter-observer disagreement in diagnosis represents a significant challenge in traditional endodontic practice , AI-based systems process digital radiography and Cone-Beam Computed Tomography (CBCT) data to render this process more objective and standardized. Particularly, Convolutional Neural Networks (CNN) offer expert-level decision support mechanisms for clinicians by demonstrating accuracy rates exceeding 90% in the detection of periapical lesions , analysis of root canal morphology , and diagnosis of vertical root fractures. AI also operates with high precision in critical phases such as working length measurement , predicting treatment prognosis , and monitoring indirect restorations. Despite these technological advantages, ethical and legal challenges including data security, algorithmic transparency (Explainable AI), and medical accountability constitute barriers to full integration within endodontics. In the future, AI combined with robotic systems and molecular biomarker analysis will initiate the era of "Precision Endodontics," rendering treatment success more predictable.
Referanslar
Russell SJN, Peter. Artificial Intelligence: A Modern Approach. 3rd ed. Upper Saddle River, NJ: Pearson; 2010.
Kaul V, Enslin S, Gross SA. History of artificial intelligence in medicine. Gastrointest Endosc 2020;92(4):807-812.doi:10.1016/j.gie.2020.06.040
Kuntz D, Wilson AK. Machine learning, artificial intelligence, and chemistry: How smart algorithms are reshaping simulation and the laboratory. Pure and Applied Chemistry 2022;94(8):1019-1054.doi: 10.1515/pac-2022-0402
Obermeyer Z, Emanuel EJ. Predicting the Future - Big Data, Machine Learning, and Clinical Medicine. N Engl J Med 2016;375(13):1216-1219.doi:10.1056/NEJMP1606181
Schwendicke F, Samek W, Krois J. Artificial Intelligence in Dentistry: Chances and Challenges. J Dent Res 2020;99(7):769-774.doi: 10.1177/0022034520915714
Goldman M, Pearson AH, Darzenta N. Endodontic success—who's reading the radiograph? Oral Surgery, Oral Medicine, Oral Pathology 1972;33(3):432-437.doi:10.1016/0030-4220(72)90473-2
Sprawls P. Physical Principles of Medical Imaging. 2nd ed. Madison, WI: Medical Physics Publishing; 1995.
Orhan K, Bayrakdar IS, Ezhov M, Kravtsov A, Ozyurek T. Evaluation of artificial intelligence for detecting periapical pathosis on cone-beam computed tomography scans. Int Endod J 2020;53(5):680-689.doi:10.1111/iej.13265
Aminoshariae A, Kulild J, Nagendrababu V. Artificial Intelligence in Endodontics: Current Applications and Future Directions. J Endod 2021;47(9):1352-1357.doi:10.1016/j.joen.2021.06.003
Grischke J, Johannsmeier L, Eich L, Griga L, Haddadin S. Dentronics: Towards robotics and artificial intelligence in dentistry. Dent Mater 2020;36(6):765-778.doi:10.1016/j.dental.2020.03.021
Aminoshariae A, Kulild JC, Syed A. Cone-beam Computed Tomography Compared with Intraoral Radiographic Lesions in Endodontic Outcome Studies: A Systematic Review. J Endod 2018;44(11):1626-1631.doi:10.1016/j.joen.2018.08.006
Krois J, Ekert T, Meinhold L, Golla T, Kharbot B, Wittemeier A, et al. Deep Learning for the Radiographic Detection of Periodontal Bone Loss. Sci Rep 2019;9(1):8495.doi:10.1038/s41598-019-44839-3
Hiraiwa T, Ariji Y, Fukuda M, Kise Y, Nakata K, Katsumata A, et al. A deep-learning artificial intelligence system for assessment of root morphology of the mandibular first molar on panoramic radiography. Dentomaxillofac Radiol 2019;48(3):20180218.doi:10.1259/dmfr.20180218
Endres MG, Hillen F, Salloumis M, Sedaghat AR, Niehues SM, Quatela O, et al. Development of a Deep Learning Algorithm for Periapical Disease Detection in Dental Radiographs. Diagnostics 2020;10(6):430.doi:10.3390/diagnostics10060430
Akalin F, Özkan Y. Deep Learning-Based Hybrid Scenario for Classification of Periapical Lesions in Cone Beam Computed Tomography. Symmetry 2025;17(9):1392.doi:10.3390/sym17091392
Hassan W, Mehta V, Singdha NT, Karobari MI. AI in Root Canal Morphology: A Detailed Bibliometric Analysis of Research Trends and Global Contributions. Clin Exp Dent Res 2025;11(6):e70269.doi:10.1002/cre2.70269
Ahmed HMA, Versiani MA, De-Deus G, Dummer PMH. A new system for classifying root and root canal morphology. Int Endod J 2017;50(8):761-770.doi:10.1111/iej.12685
Santos-Junior AO, Fontenele RC, Neves FS, Tanomaru-Filho M, Jacobs R. A novel artificial intelligence-powered tool for automated root canal segmentation in single-rooted teeth on cone-beam computed tomography. Int Endod J 2025;58(4):658-671.doi:10.1111/iej.14200
Kun K, GonzÁLez NA, Malla G. Artificial Intelligence in The Diagnosis, Treatment, and Prognostication in Endodontics: A Comprehensive Literature Review. Eur Endod J 2025;10(6):466-478.doi:10.14744/eej.2025.83788
Fatima A, Shafi I, Afzal H, Díez IT, Lourdes DRM, Breñosa J, et al. Advancements in Dentistry with Artificial Intelligence: Current Clinical Applications and Future Perspectives. Healthcare (Basel) 2022;10(11).doi:10.3390/healthcare10112188
Abdelazim R, Fouad EM. Artificial intelligent-driven decision-making for automating root fracture detection in periapical radiographs. BDJ Open 2024;10(1):76.doi:10.1038/s41405-024-00260-1
Yang P, Guo X, Mu C, Qi S, Li G. Detection of vertical root fractures by cone-beam computed tomography based on deep learning. Dentomaxillofac Radiol 2023;52(3):20220345.doi:10.1259/dmfr.20220345
Fukuda M, Inamoto K, Shibata N, Ariji Y, Yanashita Y, Kutsuna S, et al. Evaluation of an artificial intelligence system for detecting vertical root fracture on panoramic radiography. Oral Radiol 2020;36(4):337-343.doi:10.1007/s11282-019-00409-x
Ahmed ZH, Almuharib AM, Abdulkarim AA, Alhassoon AH, Alanazi AF, Alhaqbani MA, et al. Artificial Intelligence and Its Application in Endodontics: A Review. J Contemp Dent Pract 2023;24(11):912-917.doi:10.5005/jp-journals-10024-3593
Qu Y, Wen Y, Chen M, Guo K, Huang X, Gu L. Predicting case difficulty in endodontic microsurgery using machine learning algorithms. Journal of Dentistry 2023;133:104522.doi:10.1016/j.jdent.2023.104522
Zeng X, Ding J, Yuan K, Zhan J, He C, Wu H, et al. Joint detection of dental diseases with panoramic imaging system via multi-task context integration network. Optics & Laser Technology 2025;192:113394.doi:10.1016/j.optlastec.2025.113394
Latke VN, Vaibhav. Measuring Endodontic Working Length Using Artificial Intelligence. Frontiers in Health Informatics 2024;13(2):83-96.
Saghiri MA, Garcia-Godoy F, Gutmann JL, Lotfi M, Asgar K. The reliability of artificial neural network in locating minor apical foramen: a cadaver study. J Endod 2012;38(8):1130-1134.doi:10.1016/j.joen.2012.05.004
Ardila CM, Pineda-Velez E, Vivares-Builes AM. Artificial Intelligence in Endodontic Education: A Systematic Review with Frequentist and Bayesian Meta-Analysis of Student-Based Evidence. Dent J (Basel) 2025;13(11).doi:10.3390/dj13110489
Gao X, Xin X, Li Z, Zhang W. Predicting postoperative pain following root canal treatment by using artificial neural network evaluation. Sci Rep 2021;11(1):17243.doi:10.1038/s41598-021-96777-8
Bennasar C, Garcia I, Gonzalez-Cid Y, Perez F, Jimenez J. Second Opinion for Non-Surgical Root Canal Treatment Prognosis Using Machine Learning Models. Diagnostics (Basel) 2023;13(17).doi:10.3390/diagnostics13172742
Mohammad-Rahimi H, Sohrabniya F, Ourang SA, Dianat O, Aminoshariae A, Nagendrababu V, et al. Artificial intelligence in endodontics: Data preparation, clinical applications, ethical considerations, limitations, and future directions. Int Endod J 2024;57(11):1566-1595.doi:10.1111/iej.14128
Campo L, Aliaga IJ, De Paz JF, Garcia AE, Bajo J, Villarubia G, et al. Retreatment Predictions in Odontology by means of CBR Systems. Comput Intell Neurosci 2016;2016:7485250.doi:10.1155/2016/7485250
Zhou X. Predicting Non-Surgical Root Canal Therapy Outcomes Using Machine Learning. 2025.
Keskin NB, Gunec HG, Uslu G, Tezer EO. Using artificial intelligence in the evaluation of periapical, caries, and restoration status: a new methodological and technological study. BMC Med Imaging 2025;25(1):466.doi:10.1186/s12880-025-02019-y
Doumani M, Almaqboul F, Alduwaysan SSS, Alzahrani MA, Al Ghamdi SA, Alzahrani MN, et al. Effectiveness of Artificial Intelligence in Endodontic Diagnosis and Treatment Evaluation: A Systematic Review. Cureus 2025;17(11).doi:10.7759/cureus.96091
Sunar AT, Çağan; Sır, Eda; Polater, Hülya; Bağlıoğlu, Nihal. Diş Hekimliğinde Yapay Zeka Uygulamaları (Artificial Intelligence Applications In Dentistry). Journal of Kocaeli Health and Technology University (JOKOHTU) 2024;2(3):41-57.
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(11):1234-1238.doi:10.1177/0022034519867641
Suárez A, Adanero A, Díaz-Flores García V, Freire Y, Algar J. Using a Virtual Patient via an Artificial Intelligence Chatbot to Develop Dental Students' Diagnostic Skills. Int J Environ Res Public Health 2022;19(14).doi:10.3390/ijerph19148735
Agrawal P, Nikhade P. Artificial Intelligence in Dentistry: Past, Present, and Future. Cureus 2022;14(7):e27405.doi:10.7759/cureus.27405
Ardila CM, Yadalam PK. AI and dental education. Br Dent J 2025;238(5):294.doi:10.1038/s41415-025-8514-9
Suresh AN, Shyamala Nagendran; Inginshetty, Vaishnavi. ENDO AI: A Novel Artificial Intelligence Framework for Predicting Treatment Outcomes in Endodontic Therapy. Quest Journals: Journal of Medical and Dental Science Research 2025;12(2):12-19.doi:10.35629/076X-12021219
Asgary S. Artificial Intelligence in Endodontics: A Scoping Review. Iran Endod J 2024;19(2):85-98.doi:10.22037/iej.v19i2.44842
Alaqla A, Khanagar SB, Albelaihi AI, Singh OG, Alfadley A. Application and performance of artificial intelligence-based models in the detection, segmentation and classification of periapical lesions: a systematic review. Front Dent Med 2025;6:1717343.doi:10.3389/fdmed.2025.1717343
Sadr S, Rokhshad R, Daghighi Y, Golkar M, Tolooie Kheybari F, Gorjinejad F, et al. Deep learning for tooth identification and numbering on dental radiography: a systematic review and meta-analysis. Dentomaxillofac Radiol 2024;53(1):5-21.doi:10.1093/dmfr/twad001
Mangat PC, Bhaviya. Biomarkers in endodontics and conservative dentistry: An editorial overview with latest literature and future perspectives. IP Indian Journal of Conservative and Endodontics (IJCE) 2025;10(2):71-73.doi:10.18231/j.ijce.2025.013
Ossowska A, Kusiak A, Swietlik D. Artificial Intelligence in Dentistry-Narrative Review. Int J Environ Res Public Health 2022;19(6).doi:10.3390/ijerph19063449
Nayyar N, Ojcius DM, Dugoni AA. The Role of Medicine and Technology in Shaping the Future of Oral Health. J Calif Dent Assoc 2020;48(3):127-130.
Hung KF, Yeung AWK, Bornstein MM, Schwendicke F. Personalized dental medicine, artificial intelligence, and their relevance for dentomaxillofacial imaging. Dentomaxillofac Radiol 2023;52(1):20220335.doi:10.1259/dmfr.20220335
Referanslar
Russell SJN, Peter. Artificial Intelligence: A Modern Approach. 3rd ed. Upper Saddle River, NJ: Pearson; 2010.
Kaul V, Enslin S, Gross SA. History of artificial intelligence in medicine. Gastrointest Endosc 2020;92(4):807-812.doi:10.1016/j.gie.2020.06.040
Kuntz D, Wilson AK. Machine learning, artificial intelligence, and chemistry: How smart algorithms are reshaping simulation and the laboratory. Pure and Applied Chemistry 2022;94(8):1019-1054.doi: 10.1515/pac-2022-0402
Obermeyer Z, Emanuel EJ. Predicting the Future - Big Data, Machine Learning, and Clinical Medicine. N Engl J Med 2016;375(13):1216-1219.doi:10.1056/NEJMP1606181
Schwendicke F, Samek W, Krois J. Artificial Intelligence in Dentistry: Chances and Challenges. J Dent Res 2020;99(7):769-774.doi: 10.1177/0022034520915714
Goldman M, Pearson AH, Darzenta N. Endodontic success—who's reading the radiograph? Oral Surgery, Oral Medicine, Oral Pathology 1972;33(3):432-437.doi:10.1016/0030-4220(72)90473-2
Sprawls P. Physical Principles of Medical Imaging. 2nd ed. Madison, WI: Medical Physics Publishing; 1995.
Orhan K, Bayrakdar IS, Ezhov M, Kravtsov A, Ozyurek T. Evaluation of artificial intelligence for detecting periapical pathosis on cone-beam computed tomography scans. Int Endod J 2020;53(5):680-689.doi:10.1111/iej.13265
Aminoshariae A, Kulild J, Nagendrababu V. Artificial Intelligence in Endodontics: Current Applications and Future Directions. J Endod 2021;47(9):1352-1357.doi:10.1016/j.joen.2021.06.003
Grischke J, Johannsmeier L, Eich L, Griga L, Haddadin S. Dentronics: Towards robotics and artificial intelligence in dentistry. Dent Mater 2020;36(6):765-778.doi:10.1016/j.dental.2020.03.021
Aminoshariae A, Kulild JC, Syed A. Cone-beam Computed Tomography Compared with Intraoral Radiographic Lesions in Endodontic Outcome Studies: A Systematic Review. J Endod 2018;44(11):1626-1631.doi:10.1016/j.joen.2018.08.006
Krois J, Ekert T, Meinhold L, Golla T, Kharbot B, Wittemeier A, et al. Deep Learning for the Radiographic Detection of Periodontal Bone Loss. Sci Rep 2019;9(1):8495.doi:10.1038/s41598-019-44839-3
Hiraiwa T, Ariji Y, Fukuda M, Kise Y, Nakata K, Katsumata A, et al. A deep-learning artificial intelligence system for assessment of root morphology of the mandibular first molar on panoramic radiography. Dentomaxillofac Radiol 2019;48(3):20180218.doi:10.1259/dmfr.20180218
Endres MG, Hillen F, Salloumis M, Sedaghat AR, Niehues SM, Quatela O, et al. Development of a Deep Learning Algorithm for Periapical Disease Detection in Dental Radiographs. Diagnostics 2020;10(6):430.doi:10.3390/diagnostics10060430
Akalin F, Özkan Y. Deep Learning-Based Hybrid Scenario for Classification of Periapical Lesions in Cone Beam Computed Tomography. Symmetry 2025;17(9):1392.doi:10.3390/sym17091392
Hassan W, Mehta V, Singdha NT, Karobari MI. AI in Root Canal Morphology: A Detailed Bibliometric Analysis of Research Trends and Global Contributions. Clin Exp Dent Res 2025;11(6):e70269.doi:10.1002/cre2.70269
Ahmed HMA, Versiani MA, De-Deus G, Dummer PMH. A new system for classifying root and root canal morphology. Int Endod J 2017;50(8):761-770.doi:10.1111/iej.12685
Santos-Junior AO, Fontenele RC, Neves FS, Tanomaru-Filho M, Jacobs R. A novel artificial intelligence-powered tool for automated root canal segmentation in single-rooted teeth on cone-beam computed tomography. Int Endod J 2025;58(4):658-671.doi:10.1111/iej.14200
Kun K, GonzÁLez NA, Malla G. Artificial Intelligence in The Diagnosis, Treatment, and Prognostication in Endodontics: A Comprehensive Literature Review. Eur Endod J 2025;10(6):466-478.doi:10.14744/eej.2025.83788
Fatima A, Shafi I, Afzal H, Díez IT, Lourdes DRM, Breñosa J, et al. Advancements in Dentistry with Artificial Intelligence: Current Clinical Applications and Future Perspectives. Healthcare (Basel) 2022;10(11).doi:10.3390/healthcare10112188
Abdelazim R, Fouad EM. Artificial intelligent-driven decision-making for automating root fracture detection in periapical radiographs. BDJ Open 2024;10(1):76.doi:10.1038/s41405-024-00260-1
Yang P, Guo X, Mu C, Qi S, Li G. Detection of vertical root fractures by cone-beam computed tomography based on deep learning. Dentomaxillofac Radiol 2023;52(3):20220345.doi:10.1259/dmfr.20220345
Fukuda M, Inamoto K, Shibata N, Ariji Y, Yanashita Y, Kutsuna S, et al. Evaluation of an artificial intelligence system for detecting vertical root fracture on panoramic radiography. Oral Radiol 2020;36(4):337-343.doi:10.1007/s11282-019-00409-x
Ahmed ZH, Almuharib AM, Abdulkarim AA, Alhassoon AH, Alanazi AF, Alhaqbani MA, et al. Artificial Intelligence and Its Application in Endodontics: A Review. J Contemp Dent Pract 2023;24(11):912-917.doi:10.5005/jp-journals-10024-3593
Qu Y, Wen Y, Chen M, Guo K, Huang X, Gu L. Predicting case difficulty in endodontic microsurgery using machine learning algorithms. Journal of Dentistry 2023;133:104522.doi:10.1016/j.jdent.2023.104522
Zeng X, Ding J, Yuan K, Zhan J, He C, Wu H, et al. Joint detection of dental diseases with panoramic imaging system via multi-task context integration network. Optics & Laser Technology 2025;192:113394.doi:10.1016/j.optlastec.2025.113394
Latke VN, Vaibhav. Measuring Endodontic Working Length Using Artificial Intelligence. Frontiers in Health Informatics 2024;13(2):83-96.
Saghiri MA, Garcia-Godoy F, Gutmann JL, Lotfi M, Asgar K. The reliability of artificial neural network in locating minor apical foramen: a cadaver study. J Endod 2012;38(8):1130-1134.doi:10.1016/j.joen.2012.05.004
Ardila CM, Pineda-Velez E, Vivares-Builes AM. Artificial Intelligence in Endodontic Education: A Systematic Review with Frequentist and Bayesian Meta-Analysis of Student-Based Evidence. Dent J (Basel) 2025;13(11).doi:10.3390/dj13110489
Gao X, Xin X, Li Z, Zhang W. Predicting postoperative pain following root canal treatment by using artificial neural network evaluation. Sci Rep 2021;11(1):17243.doi:10.1038/s41598-021-96777-8
Bennasar C, Garcia I, Gonzalez-Cid Y, Perez F, Jimenez J. Second Opinion for Non-Surgical Root Canal Treatment Prognosis Using Machine Learning Models. Diagnostics (Basel) 2023;13(17).doi:10.3390/diagnostics13172742
Mohammad-Rahimi H, Sohrabniya F, Ourang SA, Dianat O, Aminoshariae A, Nagendrababu V, et al. Artificial intelligence in endodontics: Data preparation, clinical applications, ethical considerations, limitations, and future directions. Int Endod J 2024;57(11):1566-1595.doi:10.1111/iej.14128
Campo L, Aliaga IJ, De Paz JF, Garcia AE, Bajo J, Villarubia G, et al. Retreatment Predictions in Odontology by means of CBR Systems. Comput Intell Neurosci 2016;2016:7485250.doi:10.1155/2016/7485250
Zhou X. Predicting Non-Surgical Root Canal Therapy Outcomes Using Machine Learning. 2025.
Keskin NB, Gunec HG, Uslu G, Tezer EO. Using artificial intelligence in the evaluation of periapical, caries, and restoration status: a new methodological and technological study. BMC Med Imaging 2025;25(1):466.doi:10.1186/s12880-025-02019-y
Doumani M, Almaqboul F, Alduwaysan SSS, Alzahrani MA, Al Ghamdi SA, Alzahrani MN, et al. Effectiveness of Artificial Intelligence in Endodontic Diagnosis and Treatment Evaluation: A Systematic Review. Cureus 2025;17(11).doi:10.7759/cureus.96091
Sunar AT, Çağan; Sır, Eda; Polater, Hülya; Bağlıoğlu, Nihal. Diş Hekimliğinde Yapay Zeka Uygulamaları (Artificial Intelligence Applications In Dentistry). Journal of Kocaeli Health and Technology University (JOKOHTU) 2024;2(3):41-57.
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(11):1234-1238.doi:10.1177/0022034519867641
Suárez A, Adanero A, Díaz-Flores García V, Freire Y, Algar J. Using a Virtual Patient via an Artificial Intelligence Chatbot to Develop Dental Students' Diagnostic Skills. Int J Environ Res Public Health 2022;19(14).doi:10.3390/ijerph19148735
Agrawal P, Nikhade P. Artificial Intelligence in Dentistry: Past, Present, and Future. Cureus 2022;14(7):e27405.doi:10.7759/cureus.27405
Ardila CM, Yadalam PK. AI and dental education. Br Dent J 2025;238(5):294.doi:10.1038/s41415-025-8514-9
Suresh AN, Shyamala Nagendran; Inginshetty, Vaishnavi. ENDO AI: A Novel Artificial Intelligence Framework for Predicting Treatment Outcomes in Endodontic Therapy. Quest Journals: Journal of Medical and Dental Science Research 2025;12(2):12-19.doi:10.35629/076X-12021219
Asgary S. Artificial Intelligence in Endodontics: A Scoping Review. Iran Endod J 2024;19(2):85-98.doi:10.22037/iej.v19i2.44842
Alaqla A, Khanagar SB, Albelaihi AI, Singh OG, Alfadley A. Application and performance of artificial intelligence-based models in the detection, segmentation and classification of periapical lesions: a systematic review. Front Dent Med 2025;6:1717343.doi:10.3389/fdmed.2025.1717343
Sadr S, Rokhshad R, Daghighi Y, Golkar M, Tolooie Kheybari F, Gorjinejad F, et al. Deep learning for tooth identification and numbering on dental radiography: a systematic review and meta-analysis. Dentomaxillofac Radiol 2024;53(1):5-21.doi:10.1093/dmfr/twad001
Mangat PC, Bhaviya. Biomarkers in endodontics and conservative dentistry: An editorial overview with latest literature and future perspectives. IP Indian Journal of Conservative and Endodontics (IJCE) 2025;10(2):71-73.doi:10.18231/j.ijce.2025.013
Ossowska A, Kusiak A, Swietlik D. Artificial Intelligence in Dentistry-Narrative Review. Int J Environ Res Public Health 2022;19(6).doi:10.3390/ijerph19063449
Nayyar N, Ojcius DM, Dugoni AA. The Role of Medicine and Technology in Shaping the Future of Oral Health. J Calif Dent Assoc 2020;48(3):127-130.
Hung KF, Yeung AWK, Bornstein MM, Schwendicke F. Personalized dental medicine, artificial intelligence, and their relevance for dentomaxillofacial imaging. Dentomaxillofac Radiol 2023;52(1):20220335.doi:10.1259/dmfr.20220335
