Journal of Oral Medicine and Pain

Korean Academy of Orofacial Pain and Oral Medicine (대한안면통증구강내과학회)
권호 표지
DOI QR코드

DOI QR Code

Application of Artificial Intelligence for the Management of Oral Diseases

  • Lee, Yeon-Hee (Department of Orofacial Pain and Oral Medicine, Kyung Hee University Dental Hospital, College and School of Dentistry Kyung Hee University)
  • Received : 2022.04.16
  • Accepted : 2022.04.25
  • Published : 2022.06.30
Download PDF
⟨ Previous Next ⟩

Abstract

Artificial intelligence (AI) refers to the use of machines to mimic intelligent human behavior. It involves interactions with humans in clinical settings, and augmented intelligence is considered as a cognitive extension of AI. The importance of AI in healthcare and medicine has been emphasized in recent studies. Machine learning models, such as genetic algorithms, artificial neural networks (ANNs), and fuzzy logic, can learn and examine data to execute various functions. Among them, ANN is the most popular model for diagnosis based on image data. AI is rapidly becoming an adjunct to healthcare professionals and is expected to be human-independent in the near future. The introduction of AI to the diagnosis and treatment of oral diseases worldwide remains in the preliminary stage. AI-based or assisted diagnosis and decision-making will increase the accuracy of the diagnosis and render treatment more precise and personalized. Therefore, dental professionals must actively initiate and lead the development of AI, even if they are unfamiliar with it.

Keywords

Acknowledgement

This study was supported by a National Research Foundation of Korea (NRF) grant funded by the Korean government (MSIT) (no. NRF-2020R1F1A1070072) and Kyung Hee University in 2021 (no. KHU20211863).

References

  1. Rajpurkar P, Chen E, Banerjee O, Topol EJ. AI in health and medicine. Nat Med 2022;28:31-38. https://doi.org/10.1038/s41591-021-01614-0
  2. Phuong NH, Kreinovich V. Fuzzy logic and its applications in medicine. Int J Med Inform 2001;62:165-173. https://doi.org/10.1016/S1386-5056(01)00160-5
  3. Yadav SS, Jadhav SM. Deep convolutional neural network based medical image classification for disease diagnosis. J Big Data 2019;6:113. https://doi.org/10.1186/s40537-019-0276-2
  4. Ghaheri A, Shoar S, Naderan M, Hoseini SS. The applications of genetic algorithms in medicine. Oman Med J 2015;30:406-416. https://doi.org/10.5001/omj.2015.82
  5. Peres MA, Macpherson LMD, Weyant RJ, et al. Oral diseases: a global public health challenge. Lancet 2019;394:249-260. Erratum in: Lancet 2019;394:1010. https://doi.org/10.1016/s0140-6736(19)31146-8
  6. Johnson DE, Burtness B, Leemans CR, Lui VWY, Bauman JE, Grandis JR. Head and neck squamous cell carcinoma. Nat Rev Dis Primers 2020;6:92. https://doi.org/10.1038/s41572-020-00224-3
  7. Grote T, Berens P. On the ethics of algorithmic decision-making in healthcare. J Med Ethics 2020;46:205-211. https://doi.org/10.1136/medethics-2019-105586
  8. Lee S, Oh SI, Jo J, Kang S, Shin Y, Park JW. Deep learning for early dental caries detection in bitewing radiographs. Sci Rep 2021;11:16807. https://doi.org/10.1038/s41598-021-96368-7
  9. Papantonopoulos G, Takahashi K, Bountis T, Loos BG. Artificial neural networks for the diagnosis of aggressive periodontitis trained by immunologic parameters. PLoS One 2014;9:e89757. https://doi.org/10.1371/journal.pone.0089757
  10. Yan H, Yu M, Xia J, et al. Diverse region-based CNN for tongue squamous cell carcinoma classification with Raman spectroscopy. IEEE Access 2020;8:127313-127328. https://doi.org/10.1109/access.2020.3006567
  11. Idrees M, Farah CS, Shearston K, Kujan O. A machine-learning algorithm for the reliable identification of oral lichen planus. J Oral Pathol Med 2021;50:946-953. https://doi.org/10.1111/jop.13226
  12. Dar-Odeh NS, Alsmadi OM, Bakri F, et al. Predicting recurrent aphthous ulceration using genetic algorithms-optimized neural networks. Adv Appl Bioinform Chem 2010;3:7-13.
  13. Delavy M, Cerutti L, Croxatto A, et al. Machine learning approach for Candida albicans fluconazole resistance detection using Matrix-Assisted Laser Desorption/Ionization Time-of-Flight Mass spectrometry. Front Microbiol 2020;10:3000. https://doi.org/10.3389/fmicb.2019.03000
  14. Choi E, Kim D, Lee JY, Park HK. Artificial intelligence in detecting temporomandibular joint osteoarthritis on orthopantomogram. Sci Rep 2021;11:10246. https://doi.org/10.1038/s41598-021-89742-y
  15. Lee KS, Kwak HJ, Oh JM, et al. Automated detection of TMJ osteoarthritis based on artificial intelligence. J Dent Res 2020;99:1363-1367. https://doi.org/10.1177/0022034520936950