PERSPECTIVES ON DIGITAL DENTISTRY

digital_dentistryesra_kulkapak

ARTIFICIAL INTELLIGENCE SUPPORTED DIAGNOSTIC AND TREATMENT SYSTEMS IN DENTISTRY

Şule Gökmen

University of Health Sciences, Gülhane Faculty of Dental Medicine, Department of Orthodontics, Ankara, Türkiye

Gökmen Ş. Artificial Intelligence Supported Diagnostic and Treatment Systems in Dentistry. In: Kul E, editor. Perspectives on Digital Dentistry. 1st ed. Ankara: Türkiye Klinikleri; 2025. p.237246.

ABSTRACT

Recent years have seen a significant transformation in the field of dentistry, largely driven by tech nological advancements. A notable development in this transformation is the integration of artificial intelligence (AI) applications into dentistry. The use of AI in dentistry has been shown to aid in the diagnosis and treatment planning of diseases, with techniques such as big data analysis, machine learn ing and deep learning being employed in this regard. In particular, imaging techniques can be analysed faster and more accurately thanks to AI algorithms, which enables early diagnosis and intervention. One of the most prominent examples of AI applications in dentistry is the analysis and interpretation of dental Xrays. AIpowered systems have been shown to achieve comparable accuracy to that of a human in detecting dental caries, periodontal disease, and other dental abnormalities, enabling dentists to formulate more effective treatment plans. This facilitates dentists in the creation of more efficacious treatment plans. Integration of AI into treatment processes is now possible. virtual simulations and threedimensional modelling techniques, for instance, assist patients in comprehending treatment pro cesses and visualising treatment outcomes in advance. This, in sum, increases patient satisfaction and strengthens adherence to treatment.

Keywords: Artificial intelligence; Dentistry; Orthodontics; Diagnosis; Treatment systems; Deep learning

Referanslar

  1. Ongsulee P. Artificial intelligence, machine learning and deep learning. Int Conf ICT Knowl Eng. Published online 2018:1-6. [Crossref]
  2. Arsiwala-Scheppach LT, Chaurasia A, Müller A, Krois J, Schwendicke F. Machine Learning in Dentistry: A Scoping Review. J Clin Med. 2023;12(3):1-23. jcm12030937 [Crossref]  [PubMed]  [PMC]
  3. Schaeffer J, Plaaf A, Tan CJ, Hoane J, Brody J, Jonathan@ cs Ualberta Ca E. veterans from the CHIP TEST and DEEP THOUGHT days at CarnegIe Mellon UnIVersIty, and IBM addItIOns. Published online 1996:95-101. [Crossref]
  4. Tauqir S. Is Artificial Intelligence Transforming Dentistry Today? J Gandhara Med Dent Sci. 2021;8(4):1. [Crossref]
  5. Corbella S, Srinivas S, Cabitza F. Applications of deep learning in dentistry. Oral Surg Oral Med Oral Pathol Oral Radiol. 2021;132(2):225-238. [Crossref]  [PubMed]
  6. Katne T, Kanaparthi A, Gotoor S, Muppirala S, Devaraju R, Gantala R. Artificial Intelligence: Demystifying Dentistry-The Future and Beyond. Int J Contemp Med Surg Radiol. 2019;4(4):6-9. [Crossref]
  7. Widmann G. Image-guided surgery and medical robotics in the cranial area. Biomed Imaging Interv J. 2007;3(1):1-9. [Crossref]  [PubMed]  [PMC]
  8. Rasteau S, Ernenwein D, Savoldelli C, Bouletreau P. Artificial intelligence for oral and maxillo-facial surgery: A narrative review. J Stomatol Oral Maxillofac Surg. 2022;123(3):276-282. [Crossref]  [PubMed]
  9. Yadalam PK, Trivedi SS, Krishnamurthi I, et al. Machine Learning Predicts Patient Tangible Outcomes After Dental Implant Surgery. IEEE Access. 2022;10(November): 131481-131488. [Crossref]
  10. 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(1):1-9. [Crossref]  [PubMed]  [PMC]
  11. Choi E, Lee S, Jeong E, Shin S, Park H, Youm S, et al. Artificial intelligence in positioning between mandibular third molar and inferior alveolar nerve on panoramic radiography. Sci Rep. 2022;12(1):1-7. [Crossref]  [PubMed]  [PMC]
  12. Lee CT, Kabir T, Nelson J, Sheng S, Meng H, Dyke T, et al. Use of the deep learning approach to measure alveolar bone level. J Clin Periodontol. 2022;49(3):260-269. [Crossref]  [PubMed]  [PMC]
  13. Bilgir E, Bayrakdar İŞ, Çelik Ö, Orhan K, Akkoca F, Sağlam H, et al. An artifıcial ıntelligence approach to automatic tooth detection and numbering in panoramic radiographs. BMC Med Imaging. 2021;21(1):1-9. [Crossref]  [PubMed]  [PMC]
  14. Chen H, Zhang K, Lyu P, Li H, Zhang L, Wu J, 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):1-11. [Crossref]  [PubMed]  [PMC]
  15. Lee JH, Han SS, Kim YH, Lee C, Kim I. Application of a fully deep convolutional neural network to the automation of tooth segmentation on panoramic radiographs. Oral Surg Oral Med Oral Pathol Oral Radiol. 2020;129(6):635-642. [Crossref]  [PubMed]
  16. Leite AF, Gerven A Van, Willems H, Beznik T, Lahoud P, Gaêta-Araujo H, et al. Artificial intelligence-driven novel tool for tooth detection and segmentation on panoramic radiographs. Clin Oral Investig. 2021;25(4):2257-2267. [Crossref]  [PubMed]
  17. Rubiu G, Bologna M, Cellina M, Ce M, Sala D, Pagani R, et al. Teeth Segmentation in Panoramic Dental X-ray Using Mask Regional Convolutional Neural Network. Appl Sci. 2023;13(13). [Crossref]
  18. Cantu AG, Gehrung S, Krois J, Chaurasia A, Rossi J, Gaudin R, et al. Detecting caries lesions of different radiographic extension on bitewings using deep learning. J Dent. 2020;100(May):103425. [Crossref]  [PubMed]
  19. Ekert T, Krois J, Meinhold L, Elhennawy K, Emara R, Golla T, et al. Deep Learning for the Radiographic Detection of Apical Lesions. J Endod. 2019;45(7):917-922. e5. [Crossref]  [PubMed]
  20. Orhan K, Bayrakdar IS, Ezhov M, Kravtsov A, Özyürek T. Evaluation of artificial intelligence for detecting periapical pathosis on cone-beam computed tomography scans. Int Endod J. 2020;53(5):680-689. [Crossref]  [PubMed]
  21. Yang P, Guo X, Mu C, Qi S, Li G. Detection of vertical root fractures by cone-beam computed tomography based on deep learning. Dentomaxillofacial Radiol. 2023;52(3). [Crossref]  [PubMed]  [PMC]
  22. Hung K, Montalvao C, Tanaka R, Kawai T, Bornstein MM. The use and performance of artificial intelligence applications in dental and maxillofacial radiology: A systematic review. Dentomaxillofacial Radiol. 2019;49(1). [Crossref]  [PubMed]  [PMC]
  23. Çoban G, Öztürk T, Hashimli N, Yağci A. Comparison between cephalometric measurements using digital manual and web-based artificial intelligence cephalometric tracing software. Dental Press J Orthod. 2022;27(4):1-26. [Crossref]  [PubMed]  [PMC]
  24. Duran GS, Gökmen Ş, Topsakal KG, Görgülü S. Evaluation of the accuracy of fully automatic cephalometric analysis software with artificial intelligence algorithm. Orthod Craniofacial Res. 2023;26(3):481-490. [Crossref]  [PubMed]
  25. Yurdakurban E, Duran GS, Görgülü S. Evaluation of an automated approach for facial midline detection and asymmetry assessment: A preliminary study. Orthod Craniofacial Res. 2021;24(S2):84-91. [Crossref]  [PubMed]
  26. Thanathornwong B. Bayesian-based decision support system for assessing the needs for orthodontic treatment. Healthcare informatics research. 2018;24(1):22-28. [Crossref]  [PubMed]  [PMC]
  27. Tong C, Liang B, Li J, Zheng Z. A Deep Automated Skeletal Bone Age Assessment Model with Heterogeneous Features Learning. J Med Syst. 2018;42(12):6-13. [Crossref]  [PubMed]
  28. Evangelista K, de Freitas Silva BS, Yamamoto-Silva FP, ValladaresNeto J, Garcia Silva MA, Soares Cevidanes LH, et al. Accuracy of artificial intelligence for tooth extraction decision-making in orthodontics: a systematic review and meta-analysis. Clin Oral Investig. 2022;26(12):6893-6905. [Crossref]  [PubMed]
  29. Radwan MT, Sin Ç, Akkaya N, Vahdettin L. Artificial intelligencebased algorithm for cervical vertebrae maturation stage assessment. Orthod Craniofacial Res. 2023;26(3):349-355. [Crossref]  [PubMed]
  30. Alotaibi G, Awawdeh M, Farook FF, Aljohani M, Aldhafiri RM, Aldhoayan M. Artificial intelligence (AI) diagnostic tools: utilizing a convolutional neural network (CNN) to assess periodontal bone level radiographically-a retrospective study. BMC Oral Health. 2022;22(1):1-7. [Crossref]  [PubMed]  [PMC]
  31. Revilla-León M, Gómez-Polo M, Barmak AB, Inam W, Kan JYK, Kois JC, et al. Artificial intelligence models for diagnosing gingivitis and periodontal disease: A systematic review. J Prosthet Dent. 2023;130(6):816-824. [Crossref]  [PubMed]
  32. Khubrani YH, Thomas D, Slator PJ, White RD, Farnell DJJ. Detection of periodontal bone loss and periodontitis from 2D dental radiographs via machine learning and deep learning : systematic review employing APPRAISE-AI and meta-analysis. 2024;(September):1-20. [Crossref]  [PubMed]  [PMC]
  33. Crocombe LA, Brennan DS, Slade GD, Loc DO. Is self interdental cleaning associated with dental plaque levels, dental calculus, gingivitis and periodontal disease? J Periodontal Res. 2012;47(2):188-197. [Crossref]  [PubMed]
  34. Shen KL, Huang CL, Lin YC, Du JK, Chen FL, Kabasawa Y, et al. Effects of artificial intelligence-assisted dental monitoring intervention in patients with periodontitis: A randomized controlled trial. J Clin Periodontol. 2022;49(10):988-998. [Crossref]  [PubMed]
  35. You FT, Lin PC, Huang CL, Wu JH, Kabasawa Y, Chen CC, et al. Artificial intelligence with counseling on the treatment outcomes and quality of life in periodontitis patients. J Periodontol. 2024;(September):1-13. [Crossref]  [PubMed]
  36. Setzer FC, Shi KJ, Zhang Z, Yan H, Yoon H, Mupparapu M, et al. Artificial Intelligence for the Computer-aided Detection of Periapical Lesions in Cone-beam Computed Tomographic Images. J Endod. 2020;46(7):987-993. [Crossref]  [PubMed]
  37. PradeepKumar AR, Shemesh H, Nivedhitha MS, Hashir MMJ, Arockiam S, Maheswari TNU, et al. Diagnosis of Vertical Root Fractures by Cone-beam Computed Tomography in Root-filled Teeth with Confirmation by Direct Visualization: A Systematic Review and Meta-Analysis. J Endod. 2021;47(8):1198-1214. [Crossref]  [PubMed]
  38. 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. [Crossref]  [PubMed]
  39. Lai G, Dunlap C, Gluskin A, Nehme WB, Azim AA. Artificial Intelligence in Endodontics. J Calif Dent Assoc. 2023;51(1). [Crossref]
  40. Saghiri MA, Asgar K, Boukani KK, Lotfi M, Aghili H, Delvarani A, et al. A new approach for locating the minor apical foramen using an artificial neural network. Int Endod J. 2012;45(3):257-265. [Crossref]  [PubMed]
  41. Qu Y, Lin Z, Yang Z, Lin H, Huang X, Gu L. Machine learning models for prognosis prediction in endodontic microsurgery. J Dent. 2022;118(January):103947. [Crossref]  [PubMed]
  42. Bds IA elwahab R, Atef Y, Ibrahim S. Limited evidence suggests artificial intelligence shows promising performance in outcomes relevant to removable and fixed prosthodontic fields. J Evid Based Dent Pract. Published online 2025:102086. [Crossref]  [PubMed]
  43. 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. [Crossref]  [PubMed]
  44. Wu J, Hin K, Hong Y, Liu Y, Kit J, Tsoi H. The use of artificial intelligence in predicting maximal intercuspal position : A feasibility study. Published online 2025. [Crossref]  [PubMed]
  45. Bernauer SA, Zitzmann NU, Joda T. Bernauer, S. A., Zitzmann, N. U., Joda, T. The Use and Performance of Artificial Intelligence in Prosthodontics: A Systematic Review. Sensors, 21(19), 6628. Published online 2021. [Crossref]  [PubMed]  [PMC]
  46. Le YL, Verdonschot EH. Performance of diagnostic systems in occlusal caries detection compared. Community Dent Oral Epidemiol. 1994;22(3):187-191. [Crossref]  [PubMed]
  47. Chaves ET, Vinayahalingam S, van Nistelrooij N, Xi T, Romero VHD, Flügge T, et al. Detection of caries around restorations on bitewings using deep learning; AI-driven secondary caries detection. J Dent. 2024;143(December 2023):104886. [Crossref]  [PubMed]
  48. Thurzo A, Jančovičová V, Hain M, Thurzo M, Novak B, Kosnáčová H, et al. Human Remains Identification Using Micro-CT, Chemometric and AI Methods in Forensic Experimental Reconstruction of Dental Patterns after Concentrated Sulphuric Acid Significant Impact. Molecules. 2022;27(13). [Crossref]  [PubMed]  [PMC]
  49. Albernaz Neves J, Antunes-Ferreira N, Machado V, Botelho J, Proença L, Quintas A, et al. An Umbrella Review of the Evidence of Sex Determination Procedures in Forensic Dentistry. J Pers Med. 2022;12(5). [Crossref]  [PubMed]  [PMC]
  50. De Tobel J, Radesh P, Vandermeulen D, Thevissen PW. An automated technique to stage lower third molar development on panoramic radiographs for age estimation: A pilot study. J Forensic Odontostomatol. 2017;35(2):42-54. [Link]