EDP Sciences logo
Submit your paper
Search
Menu
Home All issues Volume 31 / No 1 (2025) J Oral Med Oral Surg, 31 1 (2025) 7 References
Open Access
Issue
J Oral Med Oral Surg
Volume 31, Number 1, 2025
Article Number 7
Number of page(s) 10
DOI https://doi.org/10.1051/mbcb/2025008
Published online 24 March 2025
  1. Veerabhadrappa SK, Hesarghatta Ramamurthy P, Yadav S, Bin Zamzuri AT. Analysis of clinical characteristics and management of ectopic third molars in the mandibular jaw: a systematic review of clinical cases. Acta Odontol Scand 2021;79:514–522. [Google Scholar]
  2. Hashemipour MA, Tahmasbi-Arashlow M, Fahimi-Hanzaei F. Incidence of impacted mandibular and maxillary third molars: a radiographic study in a Southeast Iran population. Med Oral Patol Oral Cir Bucal 2013; 18: e140–e145. [CrossRef] [PubMed] [Google Scholar]
  3. Mahdey HM, Arora S, Wei M. Prevalence and difficulty index associated with the 3(rd) mandibular molar impaction among malaysian ethnicities: a clinico-radiographic study. J Clin Diagn Res 2015; 9: ZC65–8. [Google Scholar]
  4. Alfadil L, Almajed E. Prevalence of impacted third molars and the reason for extraction in Saudi Arabia. Saudi Dent J 2020; 32: 262–268. [Google Scholar]
  5. Swift JQ, Nelson WJ. The nature of third molars: are third molars different than other teeth? Atlas Oral Maxillofac Surg Clin North Am 2012; 20: 159–162. [PubMed] [Google Scholar]
  6. Celik ME. Deep learning based detection tool for impacted mandibular third molar teeth. Diagnostics (Basel) 2022 9; 12: 942. [Google Scholar]
  7. Fukuda M, Ariji Y, Kise Y, Nozawa M, Kuwada C, Funakoshi T, et al. Comparison of 3 deep learning neural networks for classifying the relationship between the mandibular third molar and the mandibular canal on panoramic radiographs. Oral Surg Oral Med Oral Pathol Oral Radiol 2020; 130: 336–343. [Google Scholar]
  8. Jing Q, Dai X, Wang Z, Zhou Y, Shi Y, Yang S, et al. Fully automated deep learning model for detecting proximity of mandibular third molar root to inferior alveolar canal using panoramic radiographs. Oral Surg Oral Med Oral Pathol Oral Radiol 2024; 137: 671–6782024. [Google Scholar]
  9. Kwon D, Ahn J, Kim CS, et al. A deep learning model based on concatenation approach to predict the time to extract a mandibular third molar tooth BMC Oral Health 2022; 22: 571. [Google Scholar]
  10. Ding H, Wu J, Zhao W, Matinlinna JP, Burrow MF, Tsoi JKH. Artificial intelligence in dentistry—a review. Front Dent Med 2023; 4: 1085251. [CrossRef] [PubMed] [Google Scholar]
  11. Agrawal P, Nikhade P. Artificial intelligence in dentistry: past, present, and future. Cureus 2022; 14: e27405. [Google Scholar]
  12. Leite AF, Van Gerven A, Willems H, et al. Artificial intelligence driven novel tool for tooth detection and segmentation on panoramic radiographs. Clin Oral Investig 2021; 25: 2257–2267. [Google Scholar]
  13. Schwendicke F, Golla T, Dreher M, Krois J. Convolutional neural networks for dental image diagnostics: a scoping review. J Dent 2019; 91: 103226. [CrossRef] [PubMed] [Google Scholar]
  14. Mohammad-Rahimi H, Motamedian SR, Rohban MH, Krois J, Uribe SE, Mahmoudinia E, et al. Deep learning for caries detection: a systematic review. J Dent 2022; 122: 104115. [Google Scholar]
  15. Sivari E, Senirkentli GB, Bostanci E, Guzel MS, Acici K, Asuroglu T. Deep learning in diagnosis of dental anomalies and diseases: a systematic review. Diagnostics (Basel) 2023; 13: 2512. [Google Scholar]
  16. López Alcolea J, Fernández Alfonso A, Cano Alonso R, Álvarez Vázquez A, Díaz Moreno A, García Castellanos D, et al. Diagnostic performance of artificial intelligence in chest radiographs referred from the emergency department. Diagnostics 2024; 14: 2592. [Google Scholar]
  17. Nabulsi Z, Sellergren A, Jamshy S. Deep learning for distinguishing normal versus abnormal chest radiographs and generalization to two unseen diseases tuberculosis and COVID-19. Sci Rep 2021; 11: 15523. [Google Scholar]
  18. Campanella G, Hanna MG, Geneslaw L, Miraflor A, Werneck Krauss Silva V, Busam KJ, Brogi E. Clinical-grade computational pathology using weakly supervised deep learning on whole slide images. Nat Med 2019; 25: 1301–1309. [Google Scholar]
  19. Chen H, Zhang K, Lyu P, et al. A deep learning approach to automatic teeth detection and numbering based on object detection in dental periapical films. Sci Rep 2019; 9: 1–11. [PubMed] [Google Scholar]
  20. Tuzoff DV, Tuzova LN, Bornstein MM, et al. Tooth detection and numbering in panoramic radiographs using convolutional neural networks. Dentomaxillofac Radiol 2019; 48: 20180051. [Google Scholar]
  21. Kim C, Kim D, Jeong H, et al. Automatic tooth detection and numbering using a combination of a CNN and heuristic algorithm. Appl Sci 2020; 10: 5624. [Google Scholar]
  22. Parvez MF, Kota M, Syoji K. Optimization technique combined with deep learning method for teeth recognition in dental panoramic radiographs. Sci Rep 2020; 10: 19261. [Google Scholar]
  23. Hiraiwa T, Ariji Y, Fukuda M, 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: 20180218. [Google Scholar]
  24. Zheng Z, Yan H, Setzer FC, et al. Anatomically constrained deep learning for automating dental CBCT segmentation and lesion detection. IEEE Trans Autom Sci Eng 2020; 18: 603–614. [Google Scholar]
  25. Vinayahalingam S, Xi T, Bergé S, et al. Automated detection of third molars and mandibular nerve by deep learning. Sci Rep 2019; 9: 9007. [Google Scholar]
  26. Schwendicke F, Chaurasia A, Arsiwala L, Lee JH, Elhennawy K, Jost-Brinkmann PG. Deep learning for cephalometric landmark detection: systematic review and meta-analysis. Clin Oral Investig 2021; 25: 4299–4309. [Google Scholar]
  27. Chang HJ, Lee SJ, Yong TH, et al. Deep learning hybrid method to automatically diagnose periodontal bone loss and stage periodontitis. Sci Rep 2020; 10: 7531. [Google Scholar]
  28. Li X, Zhao D, Xie J. Deep learning for classifying the stages of periodontitis on dental images: a systematic review and meta-analysis. BMC Oral Health 2023; 23: 1017. [Google Scholar]
  29. Zhang W, Li J, Li ZB, Li Z. Predicting postoperative facial swelling following impacted mandibular third molars extraction by using artificial neural networks evaluation. Sci Rep 2018; 8: 12281. [Google Scholar]
  30. Vranckx M, Van Gerven A, Willems H, et al. Artificial intelligence (AI)-driven molar angulation measurements to predict third molar eruption on panoramic radiographs. Int J Environ Res Public Health 2020; 17: 3716. [Google Scholar]
  31. Lee J, Park J, Moon SY, Lee K. Automated prediction of extraction difficulty and inferior alveolar nerve injury for mandibular third molar using a deep neural network. Appl Sci 2022; 12: 475. [Google Scholar]
  32. Maruta N, Morita KI, Harazono Y, et al. Automatic machine learning-based classification of mandibular third molar impaction status. J Oral Maxillofac Surg Med Pathol 2023; 35: 327–334. [Google Scholar]
  33. Sukegawa S, Tanaka F, Hara T. Deep learning model for analyzing the relationship between mandibular third molar and inferior alveolar nerve in panoramic radiography. Sci Rep 2022; 12: 16925. [Google Scholar]
  34. Yoo JH, Yeom HG, Shin WS, et al. Deep learning based prediction of extraction difficulty for mandibular third molars. Sci Rep 2021; 11: 1954. [Google Scholar]
  35. Faadiya AN, Widyaningrum R, Arindra PK, Diba SF. The diagnostic performance of impacted third molars in the mandible: a review of deep learning on panoramic radiographs. Saudi Dent J 2024; 36: 404–412. [Google Scholar]

Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.

Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.

Initial download of the metrics may take a while.