Artificial Intelligence in Periodontics- A Review
Abstract
Early detection of supporting periodontal tissue destruction is crucial and beneficial for establishment of the correct diagnosis and prognosis for better patient management. There is a significant amount of inter-observer heterogeneity in the way that clinicians now evaluate radiographs. The concept of machines being able to carry out human functions is known as "artificial intelligence" (AI). In order to maximize the use of these multi-level data and comprehend their interaction, AI enables the integration of many and heterogeneous data domains, such as medical/dental history, socio-demographic and clinical data, imaging data, biomolecular data, social network data, etc. The data proving use of AI in dentistry and oral care needs to be reinforced. Technological approaches like federated learning should be actively applied to dental AI tasks, and harmonization of data to improve interoperability should be actively pursued.
References
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2) A. Barr, E. A. Feigenbaum, and P. R. Cohen, The Handbook of Artificial Intelligence, vol. 1-3, William Kaufmann Inc., Los Altos, CA, 1981.
3) Turing AM, Haugeland J. Computing machinery and intelligence. MA: MIT Press Cambridge (1950).
4) McCarthy J, Minsky M, Rochester N, Shannon CE. A proposal for the dartmouth summer research project on artificial intelligence. AI magazine. (2006) 27(4):12–14. http://jmc.stanford.edu/articles/dartmouth/dartmouth.pdf
5) M Arif Wani et al. “Introduction to deep learning”. In: Advances in Deep Learning. Springer, 2020, pp. 1–11.
6) 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):337–43. doi: 10.1007/s11282-019-00409-x
7) 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):987–93. doi: 10.1016/j.joen. 2020.03.025
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9) Lee J-H, Kim D-H, Jeong S-N, Choi S-H. Detection and diagnosis of dental caries using a deep learning-based convolutional neural network algorithm. J Dent. (2018) 77:106–11. doi: 10.1016/j.jdent.2018.07.015
10) Kühnisch J, Meyer O, Hesenius M, Hickel R, Gruhn V. Caries detection on intraoral images using artificial intelligence. J Dent Res. (2021) 101(2). doi: 10.1177/ 00220345211032524
11) Tanikawa C, Yamashiro T. Development of novel artificial intelligence systems to predict facial morphology after orthognathic surgery and orthodontic treatment in Japanese patients. Sci Rep. (2021) 11(1):1–11. doi: 10.1038/s41598-020-79139-8
12) Thanathornwong B. Bayesian-based decision support system for assessing the needs for orthodontic treatment. Healthc Inform Res. (2018) 24(1):22–8. doi: 10. 4258/hir.2018.24.1.22
13) Park J-H, Hwang H-W, Moon J-H, Yu Y, Kim H, Her S-B, et al. Automated identification of cephalometric landmarks: part 1—comparisons between the latest deep-learning methods YOLOV3 and SSD. Angle Orthod. (2019) 89(6):903–9. doi: 10.2319/022019-127.1
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21) Heidari AE, Pham TT, Ifegwu I, Burwell R, Armstrong WB, Tjoson T, et al. The use of optical coherence tomography and convolutional neural networks to distinguish normal and abnormal oral mucosa. J Biophotonics. (2020) 13(3):e201900221. doi: 10. 1002/jbio.201900221
22) Poedjiastoeti W, Suebnukarn S. Application of convolutional neural network in the diagnosis of jaw tumors. Healthc Inform Res. (2018) 24(3):236–41. doi: 10.4258/ hir.2018.24.3.236
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24) Luciano C, Banerjee P, DeFanti T. Haptics-based virtual reality periodontal training simulator. Virtual Reality. 2009;13:69–85. doi:10.1007/s10055-009-0112-7.
25) Rudd K, Bertoncini C, Hinders M. Simulations of Ultrasonographic Periodontal Probe Using the Finite Integration Technique. Open Acoust J. 2009;2:1–9. doi:10.2174/1874837600902010001
26) Nakhleh MK, Quatredeniers M, Haick H. Detection of Halitosis in Breath: Between the Past, Present and Future. Oral Dis. 2017;24(5):1– 11. doi:10.1111/odi.12699
27) Feres M, Louzoun Y, Haber S, Faveri M, Figueiredo LC, Levin L, et al. Support vector machine-based differentiation between aggressive and chronic periodontitis using microbial profiles. Int Dent J. 2018;68(1):39–46. doi:10.1111/idj.12326
28) Rana A, Yauney G, Wong LC, Gupta O, Muftu A, Shah P, et al. Automated segmentation of gingival diseases from oral images. In: 2017 IEEE Healthcare Innovations and Point of Care Technologies (HI-POCT). Bethesda, MD; 2018. p. 144–7. doi:10.1109/HIC.2017.8227605.
29) Yauney G, Rana A, Wong LC, Javia P, Muftu A, Shah P, et al. Automated Process Incorporating Machine Learning Segmentation and Correlation of Oral Diseases with Systemic Health. Annu Int Conf IEEE Eng Med Biol Soc. 2019;p. 3387–93. doi:10.1109/EMBC.2019.8857965.
30) Lee J-H, Kim D-H, Jeong S-N, Choi S-H. Diagnosis and prediction of periodontally compromised teeth using a deep learning-based convolutional neural network algorithm. J Periodontal Implant Sci. (2018) 48(2):114–23. doi: 10.5051/ jpis.2018.48.2.114
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32) Huang W, Wu J, Mao Y, Zhu S, Huang GF, Petritis B, et al. Developing a periodontal disease antibody array for the prediction of severe periodontal disease using machine learning classifiers. J Periodontol. (2020) 91(2):232–43. doi: 10.1002/JPER.19-0173
33) Moayeri RS, Khalili M, Nazari M. A hybrid method to predict success of dental implants. Int J Adv Computer Sci Appl. 2016;7(5). doi:10.14569/IJACSA.2016.070501
34) Lerner H, Mouhyi J, Admakin O, Mangano F. Artificial intelligence in fixed implant prosthodontics: a retrospective study of 106 implantsupported monolithic zirconia crowns inserted in the posterior jaws of 90 patients. BMC Oral Health. 2020;20(1):1–6.
35) Takahashi T, Nozaki K, Gonda T, Mameno T, Wada M, Ikebe K, et al. Identification of dental implants using deep learning-pilot study. Int J Implant Dent. 2020;6(1):53. doi:10.1186/s40729-020-00250-6.
36) Kaushal A, Altman R, Langlotz C. 2020. Geographic distribution of us cohorts used to train deep learning algorithms. Jama. 324(12):1212-1213.
37) Roberts M, Driggs D, Thorpe M, Gilbey J, Yeung M, Ursprung S, Aviles-Rivero AI, Etmann C, McCague C, Beer L et al. 2021 Common pitfalls and recommendations for using machine learning to detect and prognosticate for covid-19 using chest radiographs and ct scans. Nature Machine Intelligence. 3(3):199-217.
38) Bonawitz K, Eichner H, Grieskamp W, Huba D, Ingerman A, Ivanov V, Kiddon C, Konečný J, Mazzocchi S, McMahan HB. 2019. Towards federated learning at scale: System design. arXiv preprint arXiv:190201046.
39) Marcos-Zambrano LJ, KaraduzovicHadziabdic K, Loncar Turukalo T, Przymus P, Trajkovik V, Aasmets O, Berland M, Gruca A, Hasic J, Hron K et al. 2021. Applications of machine learning in human microbiome studies: A review on feature selection, biomarker identification, disease prediction and treatment. Frontiers in Microbiology.
Published
2025-07-08
How to Cite
kapoor, D. A., Soni, D. P., & Soni, D. S. R. (2025). Artificial Intelligence in Periodontics- A Review. UNIVERSITY JOURNAL OF DENTAL SCIENCES, 11(2). https://doi.org/10.21276/ujds.2025.v11.i2.17
Section
Review Article
