Application of artificial intelligence in the field of dental caries

doi:10.3877/cma.j.issn.1674-1366.2023.03.002

Abstract

Abstract:

The prevalence of dental caries remains high, posing a serious disease burden and a challenging situation for prevention and control. Recent advances in artificial intelligence (AI) have significantly impacted the medical field, particularly through the application of powerful image recognition technology. These advancements have provided new opportunities for developing more accurate diagnostic and risk assessment for caries, thereby enabling more nuanced and effective precision medicine. In this article, the role of AI in the diagnosis and risk prediction of caries, its research progress and prospect were discussed.

Key words: Artificial intelligence, Diagnosis, dental caries, Risk assessment, Deep learning, Machine learning

Liangyue Pang, Huancai Lin. Application of artificial intelligence in the field of dental caries[J]. Chinese Journal of Stomatological Research(Electronic Edition), 2023, 17(03): 162-166.

/ / Recommend

Add to citation manager EndNote|Ris|BibTeX

URL: https://zhkqyxyjzz.cma-cmc.com.cn/EN/10.3877/cma.j.issn.1674-1366.2023.03.002

https://zhkqyxyjzz.cma-cmc.com.cn/EN/Y2023/V17/I03/162

References 34

[1]	GBD 2016 Disease and Injury Incidence and Prevalence Collaborators. Global，regional，and national incidence，prevalence，and years lived with disability for 328 diseases and injuries for 195 countries，1990-2016：A systematic analysis for the Global Burden of Disease Study 2016[J]. Lancet，2017,390(10100):1211-1259. DOI：10.1016/S0140-6736(17)32154-2.
[2]	Righolt AJ, Jevdjevic M, Marcenes W，et al. Global-，regional-，and country-level economic impacts of dental diseases in 2015[J]. J Dent Res，2018,97(5):501-507. DOI：10.1177/0022034517750572.
[3]	Du MQ, Li Z, Jiang H，et al. Dental caries status and its associated factors among 3- to 5-year-old children in China：A national survey[J]. Chin J Dent Res，2018,21(3):167-179. DOI：10.3290/j.cjdr.a41076.
[4]	Gao YB, Hu T, Zhou XD，et al. Dental caries in Chinese elderly people：Findings from the 4th national oral health survey[J]. Chin J Dent Res，2018,21(3):213-220. DOI：10.3290/j.cjdr.a41077.
[5]	Si Y, Tai BJ, Hu DY，et al. Oral health status of Chinese residents and suggestions for prevention and treatment strategies[J]. Global Health Journal，2019,3(2):50-54. DOI：10.1016/j.glohj.2019.06.004.
[6]	Nikitas A, Michalakopoulou K, Njoya TE，et al. Artificial intelligence，transport and the smart city：Definitions and dimensions of a new mobility era[J]. Sustainability，2020,12:2789. DOI：10.3390/su12072789.
[7]	Sirsat MS, Fermé E, Câmara J. Machine learning for brain stroke：A review[J]. J Stroke Cerebrovasc Dis，2020,29(10):105162. DOI：10.1016/j.jstrokecerebrovasdis.2020.105162.
[8]	刘蓬然，霍彤彤，陆林，等. 人工智能在医学中的应用现状与展望[J]. 中华医学杂志，2021,101(44):3677-3683. DOI：10.3760/cma.j.cn112137-20210313-00628.
[9]	梁景平. 龋病早期诊断新技术的研究与应用[J]. 中华口腔医学杂志，2021,56(1):33-38. DOI：10.3760/cma.j.cn112144-20201108-00558.
[10]	Bader JD, Shugars DA, Bonito AJ. Systematic reviews of selected dental caries diagnostic and management methods[J]. J Dent Educ，2001,65(10):960-968.
[11]	帕克扎提·色依提，王铁梅，徐子能，等. 基于深度学习在曲面体层图像中人工智能辅助诊断系统初步研究[J]. 口腔医学研究，2021,37(9):845-849. DOI：10.13701/j.cnki.kqyxyj.2021.09.016.
[12]	Lee JH, Kim DH, Jeong SN，et al. Detection and diagnosis of dental caries using a deep learning-based convolutional neural network algorithm[J]. J Dent，2018,77:106-111. DOI：10.1016/j.jdent.2018.07.015.
[13]	Cantu AG, Gehrung S, Krois J，et al. Detecting caries lesions of different radiographic extension on bitewings using deep learning[J]. J Dent，2020,100:103425. DOI：10.1016/j.jdent.2020.103425.
[14]	de Araujo Faria V, Azimbagirad M, Arruda GV，et al. Prediction of radiation-related dental caries through PyRadiomics features and artificial neural network on panoramic radiography[J]. J Digit Imaging，2021,34(5):1237-1248. DOI：10.1007/s10278-021-00487-6.
[15]	Mao YC, Chen TY, Chou HS，et al. Caries and restoration detection using bitewing film based on transfer learning with CNNs[J]. Sensors （Basel），2021,21(13):4613. DOI：10.3390/s21134613.
[16]	Bayraktar Y, Ayan E. Diagnosis of interproximal caries lesions with deep convolutional neural network in digital bitewing radiographs[J]. Clin Oral Investig，2022,26(1):623-632. DOI：10.1007/s00784-021-04040-1.
[17]	Duong DL, Nguyen QDN, Tong MS，et al. Proof-of-concept study on an automatic computational system in detecting and classifying occlusal caries lesions from smartphone color images of unrestored extracted teeth[J]. Diagnostics （Basel），2021,11(7):1136. DOI：10.3390/diagnostics11071136.
[18]	Duong DL, Kabir MH, Kuo RF. Automated caries detection with smartphone color photography using machine learning[J]. Health Informatics J，2021,27(2):14604582211007530. DOI：10.1177/14604582211007530.
[19]	Kühnisch J, Meyer O, Hesenius M，et al. Caries detection on intraoral images using artificial intelligence[J]. J Dent Res，2022,101(2):158-165. DOI：10.1177/00220345211032524.
[20]	Ding B, Zhang Z, Liang Y，et al. Detection of dental caries in oral photographs taken by mobile phones based on the YOLOv3 algorithm[J]. Ann Transl Med，2021,9(21):1622. DOI：10.21037/atm-21-4805.
[21]	Liu L, Xu J, Huan Y，et al. A smart dental health-iot platform based on intelligent hardware，deep learning，and mobile terminal[J]. IEEE J Biomed Health Inform，2020,24(3):898-906. DOI：10.1109/JBHI.2019.2919916.
[22]	Ghaedi L, Gottlieb R, Sarrett DC，et al. An automated dental caries detection and scoring system for optical images of tooth occlusal surface[J]. Conf Proc IEEE Eng Med Biol Soc，2014:1925-1928. DOI：10.1109/EMBC.2014.6943988.
[23]	Jones KA, Jones N, Tenuta LMA，et al. Convolution neural networks and targeted fluorescent nanoparticles to detect and ICDAS score caries[J]. Caries Res，2022,56(4):419-428. DOI：10.1159/000527118.
[24]	Casalegno F, Newton T, Daher R，et al. Caries detection with near-infrared transillumination using deep learning[J]. J Dent Res，2019,98(11):1227-1233. DOI：10.1177/0022034519871884.
[25]	Rossi JG, Rojas-Perilla N, Krois J，et al. Cost-effectiveness of artificial intelligence as a decision-support system applied to the detection and grading of melanoma，dental caries，and diabetic retinopathy[J]. JAMA Netw Open，2022,5(3):e220269. DOI：10.1001/jamanetworkopen.2022.0269.
[26]	Schwendicke F, Mertens S, Cantu AG，et al. Cost-effectiveness of AI for caries detection：Randomized trial[J]. J Dent，2022,119:104080. DOI：10.1016/j.jdent.2022.104080.
[27]	Sadegh-Zadeh SA, Qeranqayeh AR, Benkhalifa E，et al. Dental caries risk assessment in children 5 years old and under via machine learning[J]. Dent J （Basel），2022,10(9):164. DOI：10.3390/dj10090164.
[28]	Wang Y, Wang S, Wu CY，et al. Oral microbiome alterations associated with early childhood caries highlight the importance of carbohydrate metabolic activities[J]. mSystems，2019,4(6):e00450-19. DOI：10.1128/mSystems.00450-19.
[29]	Zaorska K, Szczapa T, Borysewicz-Lewicka M，et al. Prediction of early childhood caries based on single nucleotide polymorphisms using neural networks[J]. Genes （Basel），2021,12(4):462. DOI：10.3390/genes12040462.
[30]	Qu X, Zhang C, Houser SH，et al. Prediction model for early childhood caries risk based on behavioral determinants using a machine learning algorithm[J]. Comput Methods Programs Biomed，2022,227:107221. DOI：10.1016/j.cmpb.2022.107221.
[31]	Pang L, Wang K, Tao Y，et al. A new model for caries risk prediction in teenagers using a machine learning algorithm based on environmental and genetic factors[J]. Front Genet，2021,12:636867. DOI：10.3389/fgene.2021.636867.
[32]	Liu L, Wu W, Zhang SY，et al. Dental caries prediction based on a survey of the oral health epidemiology among the geriatric residents of Liaoning，China[J]. Biomed Res Int，2020,2020:5348730. DOI：10.1155/2020/5348730.
[33]	Njimbouom SN, Lee K, Kim JD. MMDCP：Multi-modal dental caries prediction for decision support system using deep learning[J]. Int J Environ Res Public Health，2022,19(17):10928. DOI：10.3390/ijerph191710928.
[34]	Hur SH, Lee EY, Kim MK，et al. Machine learning to predict distal caries in mandibular second molars associated with impacted third molars[J]. Sci Rep，2021,11(1):15447. DOI：10.1038/s41598-021-95024-4.

Please choose a citation manager

Content to export