VIRTUAL REALITY AND DENTISTRY

Authors

  • ANAND BHATNAGAR Department of Periodontics, Jaipur Dental College Jaipur Rajasthan
  • EVA BHATNAGAR Bhatnagar Hospital Jaipur Rajasthan

DOI:

https://doi.org/10.22159/ijcpr.2023v15i3.3005

Keywords:

Virtual reality, Dentistry, Oral hygiene

Abstract

The field of dentistry could benefit considerably from virtual reality (VR). There will be several ways like Virtual reality can be used to teach patients about oral hygiene and dental procedures in a more dynamic and engaging way. Patients can utilize virtual reality (VR) to learn about various dental procedures, view 3D representations of their teeth and gums and comprehend how poor oral hygiene affects their general health. Many people experience fear or worry when going to the dentist, which can prevent them from getting the essential dental care. VR can reduce these anxieties by fostering a more tranquil and immersive atmosphere. For instance, patients might utilize virtual reality (VR) headsets to divert their attention during treatments or imagine serene settings to lessen anxiety. Virtual reality can also be utilized to educate and teach dental practitioners in a more effective and efficient manner. Before working on actual patients, students can perform a variety of dental operations in a virtual setting, which helps them develop their confidence and competence. VR can be used by dental practitioners to learn about new methods, instruments, and technologies. In general, the use of virtual reality in dentistry has the potential to enhance dental professional training and education, patient results, and patient involvement and satisfaction.

Downloads

Download data is not yet available.

References

Yu KH, Beam AL, Kohane IS. Artificial intelligence in healthcare. Nat Biomed Eng. 2018;2(10):719-31. doi: 10.1038/s41551-018-0305-z, PMID 31015651.

Topol EJ. Deep medicine: How. Artificial intelligence can make healthcare human again. 1st ed. New York: Basic Books; 2019.

Russell SJ, Norvig P. Artificial intelligence: a modern approach. 3rd ed. Englewood Cliffs: Prentice Hall; 2010.

Muller J, Massaron L. Artificial intelligence for dummies. Hoboken, NJ: John Wiley & Sons; 2018.

James G, Witten D, Hastie T, Tibshirani R. An introduction to statistical learning with applications in R. New York: Springer; 2013.

Goodfellow I, Bengio Y, Courville A. Deep learning. 1st ed. Cambridge, MA: MIT Press; 2016.

Nielsen MA. Neural networks and deep learning. Determination press; 2015. Available: http://neuralnetworksanddeeplearning.com [Last accessed on 16 Apr 2021].

Zhang K, Wu J, Chen H, Lyu P. An effective teeth recognition method using label tree with cascade network structure. Comput Med Imaging Graph. 2018;68:61-70. doi: 10.1016/j.compmedimag.2018.07.001, PMID 30056291.

Tuzoff DV, Tuzova LN, Bornstein MM, Krasnov AS, Kharchenko MA, Nikolenko SI. Tooth detection and numbering in panoramic radiographs using convolutional neural networks. Dentomaxillofac Radiol. 2019;48(4):20180051. doi: 10.1259/dmfr.20180051, PMID 30835551.

Lee JH, Kim DH, Jeong SN, Choi SH. 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, PMID 30056118.

Bader JD, Shugars DA, Bonito AJ. Systematic reviews of selected dental caries diagnostic and management methods. J Dent Educ. 2001;65(10):960-8. doi: 10.1002/j.0022-0337.2001.65.10.tb03470.x, PMID 11699997.

Xie X, Wang L, Wang A. Artificial neural network modeling for deciding if extractions are necessary prior to orthodontic treatment. Angle Orthod. 2010;80(2):262-6. doi: 10.2319/111608-588.1, PMID 19905850.

Jung SK, Kim TW. New approach for the diagnosis of extractions with neural network machine learning. Am J Orthod Dentofacial Orthop. 2016;149(1):127-33. doi: 10.1016/j.ajodo.2015.07.030, PMID 26718386.

Armitage GC. Development of a classification system for periodontal diseases and conditions. Ann Periodontol. 1999;4(1):1-6. doi: 10.1902/annals.1999.4.1.1, PMID 10863370.

Armitage GC. Learned and unlearned concepts in periodontal diagnostics: a 50 y perspective. Periodontol 2000. 2013;62(1):20-36. doi: 10.1111/prd.12006, PMID 23574462.

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(3):e89757. doi: 10.1371/journal.pone.0089757, PMID 24603408.

Papantonopoulos G, Takahashi K, Bountis T, Loos BG. Using cellular automata experiments to model periodontitis: a first step towards understanding the nonlinear dynamics of the disease. Int J Bifürcation Chaos. 2013;23(3):1350056. doi: 10.1142/S0218127413500569.

Papantonopoulos G, Takahashi K, Bountis T, Loos BG. Mathematical modeling suggests that periodontitis behaves as a non-linear chaotic dynamical process. J Periodontol. 2013;84(10):e29-39. doi: 10.1902/jop.2013.120637, PMID 23537122.

Lee JH, Kim DH, Jeong SN, Choi SH. 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. PMID 29770240.

Hiraiwa T, Ariji Y, Fukuda M, Kise Y, Nakata K, Katsumata A. 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, PMID 30379570.

Zhang X, Xiong S, Ma Y, Han T, Chen X, Wan F. A cone-beam computed tomographic study on mandibular first molars in a Chinese subpopulation. PLOS ONE. 2015;10(8):e0134919. doi: 10.1371/journal.pone.0134919, PMID 26241480.

Xue Y, Zhang R, Deng Y, Chen K, Jiang T. A preliminary examination of the diagnostic value of deep learning in hip osteoarthritis. PLOS ONE. 2017;12(6):e0178992. doi: 10.1371/journal.pone.0178992, PMID 28575070.

Wang X, Yang W, Weinreb J, Han J, Li Q, Kong X. Searching for prostate cancer by fully automated magnetic resonance imaging classification: deep learning versus non-deep learning. Sci Rep. 2017;7(1):15415. doi: 10.1038/s41598-017-15720-y, PMID 29133818.

Trebeschi S, van Griethuysen JJM, Lambregts DMJ, Lahaye MJ, Parmar C, Bakers FCH. Deep learning for fully-automated localization and segmentation of rectal cancer on multiparametric MR. Sci Rep. 2017;7(1):5301. doi: 10.1038/s41598-017-05728-9, PMID 28706185.

Published

15-05-2023

How to Cite

BHATNAGAR, A., and E. BHATNAGAR. “VIRTUAL REALITY AND DENTISTRY”. International Journal of Current Pharmaceutical Research, vol. 15, no. 3, May 2023, pp. 6-8, doi:10.22159/ijcpr.2023v15i3.3005.

Issue

Section

Review Article(s)