Benefits of artificial intelligence in dental caries identification: integrative review

Authors

DOI:

https://doi.org/10.33448/rsd-v10i2.12117

Keywords:

Dental Caries; Oral Health; Diagnosis; Artificial intelligence.

Abstract

Artificial Intelligence (AI) is a software-derived mechanism that aims to mimetize human beings cognitive functions. Currently, the search for its use grows rapidly in the health sector, covering several areas, including dentistry. Dental caries is a dynamic, multifactorial and biofilm-mediated disease that results in phasic demineralization and remineralization of dental tissues. Dental caries is one of the chronic diseases that most affect people around the world. The goal of this article is to perform an integrative review of the current literature on AI in the identification of caries, emphasizing its benefits, limitations, relevance and impact. Thus, the guiding question is: how feasible can the use of a more advanced technology such as AI for the diagnosis of cavities? The research was initiated through a search in the electronic database PubMed, Web of Science, Scopus and Cochrane, using the descriptors: “dental caries”, “oral health, diagnosis” and “artificial intelligence” indexed in the period from 2009 to 2020. After the eligibility criteria, 10 articles published in English, Portuguese or Spanish were analyzed. This integrative review was able to gather recent studies that accentuate the effect of current artificial intelligence methods on oral health, showing its aid to a dentist’s work, enhancing the diagnosis’s quality, precision and ease, thus obtaining greater efficacy in the treatment.

References

Javed, S. et al. Development of artificial neural network model for prediction of post-streptococcus mutans in dental caries. Scopus, 2019. https://www.sciencedirect.com/science/article/pii/S0169260719316347

Filho, J. C. B. L et al. Methods for detection of dental caries: from traditional to new technologies for clinical use. PubMed, 2011. http://files.bvs.br/upload/S/1983-5183/2012/v23n3/a2769.pdf

Schwendicke, Falk et al. Convolutional neural networks for dental image diagnostics: A scoping review. Scopus, 2019. https://www.sciencedirect.com/science/article/pii/S0300571219302283

Lee, Jae-Hong et al. Detection and diagnosis of dental caries using a deep learning-based convolutional neural network algorithm. Web of Science, 2018. https://www.sciencedirect.com/science/article/abs/pii/S0300571218302252

Chen, Yo-Wei et al. Artificial intelligence in dentistry: current applications and future perspectives. PubMed, 2020. https://pubmed.ncbi.nlm.nih.gov/32020135/

Hung, Man et al. Application of machine learning for diagnostic prediction of root caries. Web of Science, 2019. : https://www.researchgate.net/profile/Man_Hung2/publication/334258703_Application_of_machine_learning_for_diagnostic_prediction_of_root_caries/links/5ee4ba8ea6fdcc73be7815ad/Application-of-machine-learning-for-diagnostic-prediction-of-root-caries.pdf

Casalegno, F et al.Caries Detection with Near-Infrared Transillumination Using Deep Learning. PubMed, 2020. https://pubmed.ncbi.nlm.nih.gov/31449759/

Barbosa, Flávio de Souza et al. Using a neural network for supporting radiographic diagnosis of dental caries, Applied Artificial Intelligence:An International Journal, 2009. https://www.tandfonline.com/doi/abs/10.1080/08839510903246757

Charvát, J et al. Diffuse reflectance spectroscopy in dental caries detection and classification. Signal, ImageandVideo, 2020. https://doi.org/10.1007/s11760-020-01640-4

You, W et al. Deep learning-based dental plaque detection on primary teeth: a comparison with clinical assessments. BMC Oral Health, 2020. https://doi.org/10.1186/s12903-020-01114-6

Park, WookJoo et al. History and application of artificial neural networks in dentistry. European journal of dentistry, 2018. https://doi.org/10.4103/ejd.ejd_325_18

Prados-Privado, M et al. Dental Caries Diagnosis and Detection Using Neural Networks: A Systematic Review. Journal of Clinical Medicine, 2020. https://doi.org/10.3390/jcm9113579

Orhan, K. et al. Evaluation of artificial intelligence for detecting periapical pathosison cone‐beam computed tomography scans. International Endodontic Journal, 2020. https://onlinelibrary.wiley.com/doi/abs/10.1111/iej.13265

Kositbowornchai, S. et al. An Artificial Neural Network for Detection of Simulated Dental Caries.International Journal of Computer Assisted Radiology and Surgery, 2006. https://doi.org/10.1007/s11548-006-0040-x

Yüzbaşıoğlu, E. Attitudes and perceptions of dental students towards artificial intelligence. Journal of Dental Education, 2020. https://doi.org/10.1002/jdd.12385

Pereira A. S. et al. (2018). Metodologia da pesquisa científica. UAB/NTE/UFSM. https://repositorio.ufsm.br/bitstream/handle/1/15824/Lic _Computacao_Metodologia-Pesquisa-Cientifica.pdf?sequence=1

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Published

09/02/2021

How to Cite

CABRAL, B. M. de S.; MARQUES, A. B. C.; MENEZES, M. R. A. de .; ALVES-SILVA, E. G.; SÁ, R. A. G. de .; MELO, E. L. de .; GERBI, M. E. M. de M. .; BISPO, M. E. A. . Benefits of artificial intelligence in dental caries identification: integrative review . Research, Society and Development, [S. l.], v. 10, n. 2, p. e18310212117, 2021. DOI: 10.33448/rsd-v10i2.12117. Disponível em: https://rsdjournal.org/index.php/rsd/article/view/12117. Acesso em: 24 dec. 2024.

Issue

Vol. 10 No. 2

Section

Review Article

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Copyright (c) 2021 Bianca Maria de Souza Cabral; Anna Beathriz Cariolano Marques; Maria Regina Almeida de Menezes; Esdras Gabriel Alves-Silva; Renata Araújo Gomes de Sá; Eloiza Leonardo de Melo; Marleny Elizabeth Márquez de Martínez Gerbi; Mávio Eduardo Azevedo Bispo

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