人工智能在儿童口腔医学中的应用和展望

摘要/Abstract

摘要：

本文旨在探讨人工智能在儿童口腔医学中的应用现状、存在的问题以及未来发展方向。深度学习模型在部分口腔影像疾病诊断方面与专家诊断结果差异无统计学意义,智能口腔清洁工具已实现个性化清洁方案设计,深度学习模型在龋齿风险预测、牙龄预测方面同样表现良好。此外,人工智能在儿童牙科焦虑、疼痛控制、儿童口腔正畸领域也有其独特应用。但其应用也面临一些挑战,如数据质量、数量、管理,以及医学伦理、法律等问题。未来,应注重数据质量提升、算法优化、伦理法规完善,以及加强在辅助治疗和医生培养等方面的研究。

关键词: 人工智能, 儿童口腔医学, 深度学习

Abstract:

To explore the current applications, existing challenges, and future prospects of artificial intelligence (AI) in pediatric dentistry. Deep learning models have demonstrated a diagnostic performance in certain dental imaging-related diseases that is statistically comparable to expert assessments. Intelligent oral hygiene devices are now capable of generating personalized cleaning strategies. Deep learning techniques have also shown strong potential in caries risk prediction and dental age estimation. Moreover, AI exhibits unique applications in managing pediatric dental anxiety, pain control, and orthodontics. Nevertheless, several challenges remain, including issues related to data quality, volume, and management, as well as concerns involving medical ethics and legal regulations. Future efforts should emphasize enhancing data quality, optimizing algorithms, establishing robust ethical and regulatory frameworks, and strengthening research on AI-assisted treatment and clinical training.

Key words: artificial intelligence, pediatric dentistry, deep learning

中图分类号:

R788

查昶玮, 张曦文, 罗惜元, 刘瑞军, 王晨, 陈潇童, 于鹏. 人工智能在儿童口腔医学中的应用和展望[J]. 口腔医学, 2026, 46(6): 466-470.

ZHA Changwei, ZHANG Xiwen, LUO Xiyuan, LIU Ruijun, WANG Chen, CHEN Xiaotong, YU Peng. The application and prospect of artificial intelligence in pediatric dentistry[J]. Stomatology, 2026, 46(6): 466-470.

参考文献 42

[1]	陈旭. 儿童口腔医学发展现状与未来展望[J]. 中国实用口腔科杂志, 2019, 12(7):385-389. doi: 10.19538/j.kq.2019.07.001
[2]	Gupta R, Srivastava D, Sahu M, et al. Artificial intelligence to deep learning: Machine intelligence approach for drug discovery[J]. Mol Divers, 2021, 25(3): 1315-1360. doi: 10.1007/s11030-021-10217-3 pmid: 33844136
[3]	Chen M, Decary M. Artificial intelligence in healthcare: An essential guide for health leaders[J]. Healthc Manage Forum, 2020, 33(1): 10-18. doi: 10.1177/0840470419873123
[4]	Keskinbora K, Güven F. Artificial intelligence and ophthalmology[J]. Tjo, 2020, 50(1): 37-43. doi: 10.4274/tjo
[5]	Stultz C. Editorial commentary: Deep learning in cardiology: All models are wrong, but some are useful[J]. Trends Cardiovasc Med, 2022, 32(1): 42-43. doi: 10.1016/j.tcm.2020.12.001
[6]	Grischke J, Johannsmeier L, Eich L, et al. Dentronics: Towards robotics and artificial intelligence in dentistry[J]. Dent Mater, 2020, 36(6): 765-778. doi: S0109-5641(20)30076-2 pmid: 32349877
[7]	Robert N. How artificial intelligence is changing nursing[J]. Nurs Manag, 2019, 50(9): 30-39.
[8]	Vishwanathaiah S, Fageeh HN, Khanagar SB, et al. Artificial intelligence its uses and application in pediatric dentistry: A review[J]. Biomedicines, 2023, 11(3): 788. doi: 10.3390/biomedicines11030788
[9]	李若竹, 朱俊霞, 王媛媛, 等. 基于深度学习的儿童龋齿人工智能识别系统雏形的开发[J]. 中华口腔医学杂志, 2021, 56(12):1253-1260.
[10]	Aminoshariae A, Kulild J, Nagendrababu V. Artificial intelligence in endodontics: Current applications and future directions[J]. J Endod, 2021, 47(9): 1352-1357. doi: 10.1016/j.joen.2021.06.003 pmid: 34119562
[11]	Kılıc MC, Bayrakdar IS, Çelik Ö, et al. Artificial intelligence system for automatic deciduous tooth detection and numbering in panoramic radiographs[J]. Dentomaxillofac Radiol, 2021, 50(6): 20200172.
[12]	Mladenovic R, Arsic Z, Velickovic S, et al. Assessing the efficacy of AI segmentation in diagnostics of nine supernumerary teeth in a pediatric patient[J]. Diagnostics, 2023, 13(23): 3563. doi: 10.3390/diagnostics13233563
[13]	Jeong JS, Kim KS, Lee JW, et al. Efficacy of tooth brushing via a three-dimensional motion tracking system for dental plaque control in school children: A randomized controlled clinical trial[J]. BMC Oral Health, 2022, 22(1): 626. doi: 10.1186/s12903-022-02665-6
[14]	Abdul Haq J, Splieth CH, Mourad MS, et al. Digital application for promoting evidence-based children’s oral health to control early childhood caries: Randomized control trial on parental acceptance and efficacy[J]. J Clin Med, 2023, 12(7): 2680. doi: 10.3390/jcm12072680
[15]	Koopaie M, Salamati M, Montazeri R, et al. Salivary cystatin S levels in children with early childhood caries in comparison with caries-free children; statistical analysis and machine learning[J]. BMC Oral Health, 2021, 21(1): 650. doi: 10.1186/s12903-021-02016-x pmid: 34922509
[16]	Zaorska K, Szczapa T, Borysewicz-Lewicka M, et al. Prediction of early childhood caries based on single nucleotide polymorphisms using neural networks[J]. Genes, 2021, 12(4): 462. doi: 10.3390/genes12040462
[17]	Park YH, Kim SH, Choi YY. Prediction models of early childhood caries based on machine learning algorithms[J]. Int J Environ Res Public Health, 2021, 18(16): 8613. doi: 10.3390/ijerph18168613
[18]	Sadegh-Zadeh SA, Rahmani Qeranqayeh A, Benkhalifa E, et al. Dental caries risk assessment in children 5 years old and under via machine learning[J]. Dent J, 2022, 10(9): 164. doi: 10.3390/dj10090164
[19]	Schlickenrieder A, Meyer O, Schönewolf J, et al. Automatized detection and categorization of fissure sealants from intraoral digital photographs using artificial intelligence[J]. Diagnostics, 2021, 11(9): 1608. doi: 10.3390/diagnostics11091608
[20]	Yazar M, Aydınoğlu S, Günaçar DN. Are technological contributions in behavior guidance techniques superior to conventional methods? Effects on dental anxiety and pain perception[J]. BMC Oral Health, 2025, 25(1): 735. doi: 10.1186/s12903-025-06139-3
[21]	Do NM, Manière MC, Lefebvre F, et al. Efficacy of virtual reality hypnosis versus conscious sedation with nitrous oxide in the management of dental anxiety in pediatric dentistry: Protocol for a prospective randomized controlled trial[J]. Trials, 2025, 26(1): 201. doi: 10.1186/s13063-025-08849-z
[22]	Islam S. Evaluating the impact of AI-generated educational content on patient understanding and anxiety in endodontics and restorative dentistry: A comparative study[J]. BMC Oral Health, 2025, 25(1): 689. doi: 10.1186/s12903-025-06069-0
[23]	Yasemin M, Kasımoğlu Y, Kocaaydın S, et al. Management of dental anxiety in children using robots[J]. IEEE, 2016, 20(1):69-77.
[24]	Chakravorty S, Aulakh BK, Shil M, et al. Role of artificial intelligence (AI) in dentistry: A literature review[J]. J Pharm Bioallied Sci, 2024, 16(Suppl 1): S14-S16. doi: 10.4103/jpbs.jpbs_466_23 pmid: 38595379
[25]	Shakya RR. Artificial intelligence in pediatric dentistry[J]. J Nep Assoc Ped Dent, 2022, 3(1): 47-49. doi: 10.3126/jnapd.v3i1.50066
[26]	王聪, 秦丽红, 陈荟, 等. 人工智能技术在儿童口腔行为管理中的应用进展[J]. 山东医药, 2024, 64(33):88-91.
[27]	Mohammad-Rahimi H, Sohrabniya F, Ourang SA, et al. Artificial intelligence in endodontics: Data preparation, clinical applications, ethical considerations, limitations, and future directions[J]. Int Endodontic J, 2024, 57(11): 1566-1595. doi: 10.1111/iej.v57.11
[28]	Asgary S. Artificial intelligence in endodontics: A scoping review[J]. Iran Endod J, 2024, 19(2):85-98.
[29]	Salekin MS, Zamzmi G, Goldgof D, et al. Multimodal spatio-temporal deep learning approach for neonatal postoperative pain assessment[J]. Comput Biol Med, 2021, 129: 104150. doi: 10.1016/j.compbiomed.2020.104150
[30]	Gao N, Yao RY, Liang RH, et al. Multi-level objective alignment transformer for fine-grained oral panoramic X-ray report generation[J]. IEEE Trans Multimedia, 2024, 26: 7462-7474. doi: 10.1109/TMM.2024.3368922
[31]	曾雪晴, 夏斌, 曹战强, 等. 基于深度学习的儿童曲面体层线片牙齿数目异常识别模型的研发[J]. 中华口腔医学杂志, 2023, 58(11):1138-1144.
[32]	Nordblom NF, Büttner M, Schwendicke F. Artificial intelligence in orthodontics: Critical review[J]. J Dent Res, 2024, 103(6): 577-584. doi: 10.1177/00220345241235606
[33]	Liu JQ, Zhang CF, Shan ZY. Application of artificial intelligence in orthodontics: Current state and future perspectives[J]. Healthcare, 2023, 11(20): 2760. doi: 10.3390/healthcare11202760
[34]	Wood T, Anigbo JO, Eckert G, et al. Prediction of the post-pubertal mandibular length and Y axis of growth by using various machine learning techniques: A retrospective longitudinal study[J]. Diagnostics, 2023, 13(9): 1553. doi: 10.3390/diagnostics13091553
[35]	Zhang JN, Lu HP, Hou J, et al. Deep learning-based prediction of mandibular growth trend in children with anterior crossbite using cephalometric radiographs[J]. BMC Oral Health, 2023, 23(1): 28. doi: 10.1186/s12903-023-02734-4
[36]	Li ZY, Hung KF, Ai QYH, et al. Radiographic imaging for the diagnosis and treatment of patients with skeletal Class Ⅲ malocclusion[J]. Diagnostics, 2024, 14(5): 544. doi: 10.3390/diagnostics14050544
[37]	Kılıç B, İbrahim AH, Aksoy S, et al. A family-centered orthodontic screening approach using a machine learning-based mobile application[J]. J Dent Sci, 2024, 19(1): 186-195. doi: 10.1016/j.jds.2023.05.001 pmid: 38303845
[38]	房兵, 金作林, 白玉兴, 等. 儿童和青少年早期错牙合畸形诊治策略的专家共识[J]. 上海口腔医学, 2021, 30(5):449-455.
[39]	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
[40]	Shan T, Tay FR, Gu L. Application of artificial intelligence in dentistry[J]. J Dent Res, 2021, 100(3): 232-244. doi: 10.1177/0022034520969115 pmid: 33118431
[41]	Mohammad-Rahimi H, Motamedian SR, Rohban MH, et al. Deep learning for caries detection: A systematic review[J]. J Dent, 2022, 122: 104115. doi: 10.1016/j.jdent.2022.104115
[42]	Dhopte A, Bagde H. Smart smile: Revolutionizing dentistry with artificial intelligence[J]. Cureus, 2023, 15(6):e41227-e41227.