Journal of Shandong University (Health Sciences) ›› 2026, Vol. 64 ›› Issue (7): 118-124.doi: 10.6040/j.issn.1671-7554.0.2025.0998

• Review • Previous Articles    

Progress in applying digitalization and artificial intelligence to pediatric dentistry

HUI Ting1, ZHANG Guangcan2   

  1. 1. kanghong Clinic;
    2. Department of Prosthodontics, School and Hospital of Stomatology, Cheeloo College of Medicine, Shandong University/Shandong Key Laboratory of Oral Diseqses/Shandong Engineering Research Center of Dental Materials and Oral Tissue Regeneration/Shandong Provincial Clinical Research Center for Oral Diseases, Jinan 250012, Shandong, China
  • Online:2026-07-10 Published:2026-07-21

PDF (PC)

0

Abstract: Digitalization and artificial intelligence are two important branches of computer science. In recent years, their applications in the field of dentistry have become increasingly widespread due to the rapid development of information technology. Pediatric dentistry involves multiple disciplines and there are varying oral conditions among children of different ages. Furthermore, childrens cooperation in oral disease treatment is relatively low, which makes clinical work more difficult. Digitalization and artificial intelligence technologies are gradually becoming established in the field of pediatric dentistry. In pediatric dental imaging, deep learning models can be used to assess dental age and detectabnormalities in the number and position of teeth. With regard to pediatric dental diseases, artificial intelligence can help with the screening and detection of tooth decay, the diagnosis of maxillofacial deformities in adolescents, and the design of orthodontic treatment plans. AI can also enhance pediatric dental behavior management and assess childrens oral health status. While ensuring the effectiveness of treatment, it provides children with comfortable, personalized, and highly humane treatment plans, which is of great clinical importance in improving the efficiency of diagnosis and treatment.

Key words: Digitalization, Artificial intelligence, Pediatric dentistry

CLC Number: 

  • R788
[1] 刘洪臣. 人工智能口腔医学[J]. 中华口腔医学杂志, 2020, 55(12): 915-919.
[2] Deo R C. Machine learning in medicine[J]. Circulation, 2015, 132(20): 1920-1930.
[3] Choi R Y, Coyner A S, Kalpathy-Cramer J, et al. Introduction to machine learning, neural networks, and deep learning[J]. Transl Vis Sci Technol, 2020, 9(2): 14. DOI:10.1167/tvst.9.2.14
[4] Lee W H, Lee S, Kim J, et al. Measurement of psoriasis-affected area with artificial neural network[J]. J Am Acad Dermatol, 2023, 88(3): 731-732.
[5] Vishwanathaiah S, Fageeh H N, Khanagar S B, et al. Artificial intelligence its uses and application in pediatric dentistry: a review[J]. Biomedicines, 2023, 11(3): 788. DOI:10.3390/biomedicines11030788
[6] 曾雪晴, 樊华, 夏斌, 等. 口腔医生对人工智能辅助判读儿童曲面断层片的需求调查[J]. 牙体牙髓牙周病学杂志, 2024, 34(6): 332-336. Zeng Xueqing, Fan Hua, Xia Bin, et al. The needs of dentists for artificial intelligence models to read pediatric panoramic radiographs[J]. Chinese Journal of Conservative Dentistry, 2024, 34(6): 332-336.
[7] Zaborowicz K, Biedziak B, Olszewska A, et al. Tooth and bone parameters in the assessment of the chronological age of children and adolescents using neural modelling methods[J]. Sensors, 2021, 21(18): 6008. DOI:10.3390/s21186008
[8] Zaborowicz M, Zaborowicz K, Biedziak B, et al. Deep learning neural modelling as a precise method in the assessment of the chronological age of children and adolescents using tooth and bone parameters[J]. Sensors, 2022, 22(2): 637. DOI:10.3390/s22020637
[9] Warin K, Limprasert W, Suebnukarn S, et al. Assessment of deep convolutional neural network models for mandibular fracture detection in panoramic radiographs[J]. Int J Oral Maxillofac Surg, 2022, 51(11): 1488-1494.
[10] 石宇超, 叶泽林, 董文萱, 等. 儿童牙龄智能评估的技术创新与应用[N]. 中国医学论坛报, 2024-07-30(05-06).
[11] Kılıc MC, Bayrakdar IS, Çelik Ö, et al. Artificial intelligence system for automatic deciduous tooth detection and numbering in panoramic radiographs[J]. Dentoma-xillofac Radiol, 2021, 50(6): 20200172. DOI:10.1259/dmfr.20200172
[12] Kaya E, Gunec H G, Gokyay S S, et al. Proposing a CNN method for primary and permanent tooth detection and enumeration on pediatric dental radiographs[J]. J Clin Pediatr Dent, 2022, 46(4): 293-298.
[13] Ahn Y, Hwang J J, Jung Y H, et al. Automated mesiodens classification system sing deeplearning on panoramic radiographs of children[J]. Diagnostics(Basel), 2021, 11(8):1477. DOI:10.3390/diagnostics11081477
[14] Ha E G, Jeon K J, Kim Y H,et al. Automatic detection of mesiodens on panoramic radiographs using artificial intelligence[J]. Sci Rep,2021, 29,11(1): 23061. DOI: 10.1038/s41598-021-02571-x
[15] Kim J, Hwang J J, Jeong T, et al. Deep learning-based identification of mesiodens using automatic maxillary anterior region estimation in panoramic radiography of children[J]. Dentomaxillofacial Radiol, 2022, 51(7): 20210528. DOI:10.1259/dmfr.20210528
[16] 曾雪晴, 夏斌, 曹战强, 等. 基于深度学习的儿童曲面体层X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. Zeng Xueqing, Xia Bin, Cao Zhanqiang, et al. Identification model of tooth number abnormalities on pediatric panoramic radiographs based on deep learning[J]. Chinese Journal of Stomatology, 2023, 58(11): 1138-1144.
[17] Isensee F, Jaeger P F, Kohl S A A, et al. nnU-Net: a self-configuring method for deep learning-based biomedical image segmentation[J]. Nat Methods, 2021, 18(2): 203-211.
[18] Zhu H H, Yu H J, Zhang F, et al. Automatic segmentation and detection of ectopic eruption of first permanent molars on panoramic radiographs based on nnU-Net[J]. Int J Paediatr Dent, 2022, 32(6): 785-792.
[19] Liu J, Liu Y, Li S, Ying S,et al. Artificial intelligence-aided detection of ectopic eruption of maxillary first molars based on panoramic radiographs[J]. J Dent,2022,125:104239. DOI: 10.1016/j.jdent.2022.104239
[20] Pang L Y, Wang K T, 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
[21] Karhade D S, Roach J, Shrestha P, et al. An automated machine learning classifier for early childhood caries[J]. Pediatr Dent, 2021, 43(3): 191-197
[22] 余红兵. 一种基于深度学习的儿童六龄牙龋齿智能筛查方法[P]. 中国专利: CN112151167A.2020.12.29
[23] 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.
[24] 李彦甫, 兰海月, 薛婧帆, 等. 基于多尺度路径聚合的儿童龋齿检测算法[J]. 中国激光, 2024, 51(15): 162-172. Li Yanfu, Lan Haiyue, Xue Jingfan, et al. Child caries detection algorithm based on multi-scale path aggregation[J]. Chinese Journal of Lasers, 2024, 51(15): 162-172.
[25] 中国医师协会耳鼻咽喉头颈外科医师分会. 儿童扁桃体腺样体低温等离子射频消融术规范化治疗临床实践指南[J]. 临床耳鼻咽喉头颈外科杂志, 2021, 35(3): 193-199.
[26] Kournoutas I, Vigo V, Chae R, et al. Acquisition of volumetric models of skull baseanatomy using endoscopic endonasal approaches: 3D scanning of deep corridors via photogrammetry[J]. World Neurosurg, 2019, 129: 372-377. DOI:10.1016/j.wneu.2019.05.251
[27] Zhao T T, Zhou J W, Yan J R, et al. Automated adenoid hypertrophy assessment with lateralcephalometry in children based on artificial intelligence[J]. Diagnostics, 2021, 11(8): 1386. DOI:10.3390/diagnostics11081386
[28] Park J H, Hwang H W, Moon J H, et al. Automated identification of cephalometric landmarks: Part1-Comparisons between the latest deep-learning methods YOLOV3 and SSD[J]. Angle Orthod, 2019, 89(6): 903-909.
[29] Knoops P G M, Papaioannou A, Borghi A, et al. A machine learning framework for automated diagnosis and computer-assisted planning in plastic and reconstructive surgery[J]. Sci Rep, 2019, 9(1): 13597. DOI:10.1038/s41598-019-49506-1
[30] Thanathornwong B. Bayesian-based decision support system for assessing the needs for orthodontic treatment[J]. Healthc Inform Res, 2018, 24(1): 22-28.
[31] Kunz F, Stellzig-Eisenhauer A, Zeman F, et al. Artificial intelligence in orthodontics: evaluation of a fully automated cephalometric analysis using acustomized convolutional neural network[J]. J Orofac Orthop, 2020, 81(1): 52-68.
[32] Zanjani F G, Pourtaherian A, Zinger S, et al. Mask-MCNet: tooth instance segmentation in 3D point clouds of intra-oral scans[J]. Neurocomputing, 2021, 453: 286-298. DOI:10.1016/j.neucom.2020.06.145
[33] Hao J, Liao W, Zhang Y L, et al. Toward clinically applicable 3-dimensional tooth segmentationvia deep learning[J]. J Dent Res, 2022, 101(3): 304-311.
[34] Wu T H, Lian C F, Lee S, et al. Two-stage mesh deep learning for automated tooth segmentation and landmark localization on 3D intraoral scans[J]. IEEE Trans Med Imaging, 2022, 41(11): 3158-3166.
[35] Ma Q C, Kobayashi E, Fan B W, et al. Automatic 3D landmarking model using patch-based deep neural networks for CT image of oral and maxillofacial surgery[J]. Int J Med Robot, 2020, 16(3): e2093. DOI:10.1002/rcs.2093
[36] Lachinov D, Getmanskaya A, Turlapov V. Cephalometric landmark regression with convolutional neural networks on 3D computed tomography data[J]. Pattern Recognit Image Anal, 2020, 30(3): 512-522.
[37] Hung K, Yeung A W K, Tanaka R, et al. Current applications, opportunities, and limitations of AI for 3D imaging in dental research and practice[J]. Int J Environ Res Public Health, 2020, 17(12): 4424. DOI:10.3390/ijerph17124424
[38] American Academy of Pediatric Dentistry. Clinical guideline on behavior management[J]. Pediatr Dent, 2004, 26(7 ): 89-94.
[39] 冉龙宽, 樊林, 郁葱. 虚拟现实技术应用于儿童口腔治疗非药物行为引导的进展[J]. 中国实用口腔科杂志, 2020, 13(7): 391-395. Ran Longkuan, Fan Lin, Yu Cong. Research progress on the application of virtual reality to non-drug behavioral guidance in dental treatment for children[J]. Chinese Journal of Practical Stomatology, 2020, 13(7): 391-395.
[40] 任海霞, 刘颍凤, 梁慧敏, 等. 虚拟现实技术在儿童深龋治疗中对牙科畏惧的干预效果研究[J]. 国际口腔医学杂志, 2022, 42(5): 529-536. Ren Haixia, Liu Yingfeng, Liang Huimin, et al. Intervention effect of virtual reality technology on dental fear in the treatment of deep caries in children[J]. International Journal of Stomatology, 2022, 42(5): 529-536.
[41] Eaton J J, McTigue D J, Fields H W Jr, et al. Attitudes of contemporary parents toward behavior management techniques used in pediatric dentistry[J]. Pediatr Dent, 2005, 27(2): 107-113.
[42] Patini R, Staderini E, Cantiani M, et al. Dental anaesthesia for children-effects of a computer-controlled delivery system on pain and heart rate: a randomised clinical trial[J]. Br J Oral Maxillofac Surg, 2018, 56(8): 744-749.
[43] Tai M C. The impact of artificial intelligence on human society and bioethics[J]. Tzu Chi Med J, 2020, 32(4): 339-343.
[44] Schwendicke F, Samek W, Krois J. Artificial intelligence in dentistry: chances and challenges[J]. J Dent Res, 2020, 99(7): 769-774.
[45] Khan B, Fatima H, Qureshi A, et al. Drawbacks of artificial intelligence and their potential solutions in the healthcare sector[J]. Biomed Mater Devices, 2023: 1-8.
[46] You W Z, Hao A M, Li S, et al. Deep learning-based dental plaque detection on primary teeth: a comparison with clinical assessments[J]. BMC Oral Health, 2020, 20(1): 141. DOI:10.1186/s12903-020-01114-6
[47] 游文喆, 郝爱民, 李帅, 等. 基于深度学习的恒牙牙菌斑智能判读系统的建立及其相关影响因素分析[J]. 中华口腔医学杂志, 2021, 56(7): 665-671.
[48] 姚立伟. 科技赋能儿童口腔护理, AI互动牙刷引领行业新潮[EB/OL].(2024-04-30)[2025-08-20].https://www.sohu.com/a/775359720_114822
[49] 房禹池, 杨世波, 施麦克, 等. 基于U-Net网络与注意力机制的儿童龋齿预防算法研究[J]. 传感技术学报, 2025, 38(5): 817-825. Fang Yuchi, Yang Shibo, Shi Maike, et al. Research on a pediatric dental caries prevention algorithm utilizing U-Net network and attention mechanism[J]. Chinese Journal of Sensors and Actuators, 2025, 38(5): 817-825.
[50] 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
[1] LIANG Chen, YAO Lijuan, LYU Longfei, TANG Ze, QIN Da, CUI Youbin,YU Xiaoqi. Application of 3D reconstruction combined with CT-guided puncture localization in minimally invasive treatment of pulmonary ground-glass nodules [J]. Journal of Shandong University (Health Sciences), 2026, 64(5): 74-82.
[2] Intelligent Orthopedics Subgroup of Chinese Association of Orthopedic, Subgroup for Prevention and Control of Spinal and Spinal Cord Injury Diseases of Professional Committee for Prevention and Control of Spinal Diseases of Chinese Preventive Medicine Association. Expert consensus on measurement sites and annotation of artificial intelligence-based spinal degenerative imaging(2025) [J]. Journal of Shandong University (Health Sciences), 2026, 64(2): 1-10.
[3] JI Xinyu, YU Siyi, SUN Yuanyuan, JI Bing. Orthopedic disease diagnosis and treatment assistance methods based on artificial intelligence and gait analysis [J]. Journal of Shandong University (Health Sciences), 2026, 64(2): 34-43.
[4] WANG Jianmin, LI Xiaofeng, YOU Zhitao, DONG Shengjie, ZHAO Yuchi, LI Zhanju, ZOU Dexin, ZHANG Jianfeng, SUN Tao, DU Wei. Construction of a chronic post-surgical pain prediction model for posterior lumbar interbody fusion surgery based on interpretable machine learning [J]. Journal of Shandong University (Health Sciences), 2026, 64(2): 78-88.
[5] ZHANG Xinru, LI Yang, SUN Meng, NIE Wei, MA Zhe. Application and evaluation of Vision-LSTM model in diagnostic ultrasound imaging of Thyroid Imaging Reporting and Data System Category 4b thyroid nodules [J]. Journal of Shandong University (Health Sciences), 2025, 63(11): 68-74.
[6] WU Qiqi, CHENG Miaomiao, XIAO Xiaoyan. Multimodal models in the field of kidney disease [J]. Journal of Shandong University (Health Sciences), 2025, 63(10): 117-124.
[7] LIANG Bowen, LU Qingsheng. Advances in robotic-assisted endovascular aortic repair [J]. Journal of Shandong University (Health Sciences), 2024, 62(9): 61-65.
[8] ZHANG Jinghui, WANG Juan, ZHAO Yujie, DUAN Miao, LIU Yiran, LIN Minjuan, QIAO Xu, LI Zhen, ZUO Xiuli. Construction of a machine learning-based tongue diagnosis model for gastrointestinal diseases [J]. Journal of Shandong University (Health Sciences), 2024, 62(1): 38-47.
[9] Shiqing FENG. Computer vision and lumbar degenerative disease [J]. Journal of Shandong University (Health Sciences), 2023, 61(3): 1-6.
[10] Nan WU,Jianguo ZHANG,Yuanpeng ZHU,Guilin CHEN,Zefu CHEN. Application of artificial intelligence in the diagnosis and treatment of spinal deformity [J]. Journal of Shandong University (Health Sciences), 2023, 61(3): 14-20.
[11] Lin HUANG,Zhen CHE,Ming LI,Yuxi LI,Qing NING. Research advances of artificial intelligence in the diagnosis and treatment of orthopaedic diseases [J]. Journal of Shandong University (Health Sciences), 2023, 61(3): 37-45.
[12] WANG Hui, WANG Lianlei, WU Tianchi, TIAN Yonghao, YUAN Suomao, WANG Xia, LYU Weijia, LIU Xinyu. Artificial intelligence-assisted 3D printing of surgical guides for pedicle screw Insertion in scoliosis surgeries [J]. Journal of Shandong University (Health Sciences), 2023, 61(3): 127-133.
[13] Pei NIE,Ximing WANG. Research progress in the application of artificial intelligence in myocardial imaging [J]. Journal of Shandong University (Health Sciences), 2023, 61(12): 1-6.
[14] Ziliang XU,Minwen ZHENG. Innovation and challenge of imaging artificial intelligence in medical field [J]. Journal of Shandong University (Health Sciences), 2023, 61(12): 7-12, 20.
[15] Xiao LI,Zhiyuan SUN,Longjiang ZHANG. Research advances of artificial intelligence-based medical imaging in the screening, diagnosis and prediction of pneumonia [J]. Journal of Shandong University (Health Sciences), 2023, 61(12): 13-20.
Viewed
Full text


Abstract

Cited
  Shared   
  Discussed   
No Suggested Reading articles found!
Copyright 2018 © Editorial office of Journal of Shandong University (Health Sciences)
Supported by Beijing Magtech Co.Ltd