国际口腔医学杂志

国际口腔医学杂志 ›› 2025, Vol. 52 ›› Issue (5): 579-585.doi: 10.7518/gjkq.2025052

• 人工智能专栏 • 上一篇    下一篇

基于深度学习技术诊断龋病方法的研究进展

周小洁1(),侯本祥2()   

  1. 1.国家儿童医学中心 首都医科大学附属北京儿童医院口腔科 北京 100045
    2.首都医科大学口腔医学院口腔显微诊疗中心 北京 100162
  • 收稿日期:2024-05-21 修回日期:2024-11-15 出版日期:2025-09-01 发布日期:2025-08-27
  • 通讯作者: 侯本祥
  • 作者简介:周小洁，副主任医师，硕士，Email：dentistzhou21520@163.com
  • 基金资助:
    北京市科技计划项目(Z191100006619037)

Research progress on the methods for caries diagnosis based on deep learning

Xiaojie Zhou1(),Benxiang Hou2()   

  1. 1.Dept. of Stomatology, Beijing Children ’ s Hospital, Capital Medical University, National Center for Children ’ s Health, Beijing 100045, China
    2.Center for Microscope Enhanced Dentistry, Capital Medical University School of Stomatology, Beijing 100162, China
  • Received:2024-05-21 Revised:2024-11-15 Online:2025-09-01 Published:2025-08-27
  • Contact: Benxiang Hou
  • Supported by:
    Science and Technology Planning Project of Beijing Municipal Science & Technology Commission(Z191100006619037)

RichHTML

2

PDF (PC)

8

摘要：

龋病发病率高，及时诊断是临床治疗的基础。随着深度学习技术在自然图像处理领域的突破，基于深度学习利用口腔医学影像进行龋病自动诊断的方法受到了广泛关注。基于深度学习的龋病诊断方法在龋病识别、检测与分割两大类典型任务中取得了进展，本文从多个角度对两类任务的深度学习方法进行综述与对比，对龋病诊断数据集以及面临的挑战进行分析，有助于龋病智能诊断的研究。

关键词: 龋病诊断, 口腔医学影像分析, 深度学习

Abstract:

Dental caries have a high incidence rate, and timely diagnosis forms the basis for clinical treatment. With the breakthroughs of deep learning technology in the field of natural image processing, deep learning methods for automatic diagnosis of dental caries by utilizing oral medical images have garnered significant attention. These deep learning-based diagnostic approaches for dental caries have made strides in three primary tasks: identification, detection, and segmentation of caries. This paper provides an overview and comparative analysis with multiple perspectives of deep learning methodologies applied to these tasks, delves into the datasets utilized for caries diagnosis, and points out the challenges faced, all with the aim of facilitating intelligent diagnosis of dental caries.

Key words: caries diagnosis, oral medical imaging analysis, deep learning

中图分类号: 

  • R781.1

《牙周松牙固定术操作详解》出版发行"

[1] LeCun Y, Bengio Y, Hinton G. Deep learning[J]. Nature, 2015, 521(7553): 436-444.
[2] 李骋, 陈虎, 王勇, 等. 人工神经网络对口腔三维解剖特征分割与识别的研究进展[J]. 中华口腔医学杂志, 2022, 57(5): 540-546.
Li C, Chen H, Wang Y, et al. Labelling, segmentation and application of neural network based on machine learning of three-dimensional intraoral anatomical features[J]. Chin J Stomatol, 2022, 57(5): 540-546.
[3] 毕小琴, 赵佛容. 人工智能技术在口腔专科治疗及护理中的应用[J]. 华西口腔医学杂志, 2018, 36(4): 452-456.
Bi XQ, Zhao FR. Application of artificial intelligence in stomatology treatment and nursing[J]. West China J Stomatol, 2018, 36(4): 452-456.
[4] 张艳, 洪宇晨, 许艳华. 人工神经网络在口腔正畸学的应用[J]. 中华口腔正畸学杂志, 2023, 30(4): 216-219.
Zhang Y, Hong YC, Xu YH. Application of artificial neural network in orthodontics[J]. Chin J Orthod, 2023, 30(4): 216-219.
[5] 孙斯妤, 张涵, 杨军英, 等. 人工智能在龋病诊疗中应用的研究进展[J]. 临床口腔医学杂志, 2024, 40(6): 379-382.
Sun SY, Zhang H, Yang JY, et al. Research progress on the application of artificial intelligence in the dia-gnosis and treatment of caries[J]. J Clin Stomatol, 2024, 40(6): 379-382.
[6] 刘璐. 基于人工神经网络技术的老年龋预测模型的构建及预测方法学比较的泛化能力验证研究[D]. 沈阳: 中国医科大学, 2021.
Liu L. Construction of prediction models of dental caries in the elderly based on artificial neural network technology and validation of the generalization ability of methodology comparisons[D]. Shen-yang: China Medical University, 2021.
[7] 刘红艳, 韦曦, 凌均棨. 人工智能在龋病诊疗中的应用与探索[J]. 中华口腔医学杂志, 2024, 59(1): 37-44.
Liu HY, Wei X, Ling JQ. Application and exploration of artificial intelligence for caries management[J]. Chin J Stomatol, 2024, 59(1): 37-44.
[8] 庞亮月, 林焕彩. 人工智能在龋病诊疗中的应用[J]. 中华口腔医学研究杂志(电子版), 2023, 17(3): 162-166.
Pang LY, Lin HY. Application of artificial intelligence in the field of dental caries[J]. Chin J Stomatol (Electr Ed), 2023, 17(3): 162-166.
[9] Forouzeshfar P, Safaei AA, Ghaderi F, et al. Dental caries diagnosis using neural networks and deep learning: a systematic review[J]. Multimed Tools Appl, 2023, 83(10): 30423-30466.
[10] Mohammad-Rahimi H, Motamedian SR, Rohban MH, et al. Deep learning for caries detection: a systematic review[J]. J Dent, 2022, 122: 104115.
[11] Geetha V, Aprameya KS, Hinduja DM. Dental ca-ries diagnosis in digital radiographs using back-pro-pagation neural network[J]. Health Inf Sci Syst, 2020, 8(1): 8.
[12] Vinayahalingam S, Kempers S, Limon L, et al. Classification of caries in third molars on panoramic radiographs using deep learning[J]. Sci Rep, 2021, 11: 12609.
[13] Imangaliyev S, van der Veen MH, Volgenant CMC, et al. Deep learning for classification of dental plaque images[C]//Nicosia G, Giuffrida G, Conca P, et al. Machine learning, optimization, and big data-2nd International Workshop, MOD 2016. Cham: Springer International Publishing AG, 2016: 407-410.
[14] Lee JH, Kim DH, Jeong SN, et al. Detection and dia-gnosis of dental caries using a deep learning-based convolutional neural network algorithm[J]. J Dent, 2018, 77: 106-111.
[15] Karimian N, Salehi HS, Mahdian M, et al. Deep learning classifier with optical coherence tomography images for early dental caries detection[C]//Rechmann P, Fried D. Lasers in Dentistry XXIV, 2018. San Francisco: SPIE BIOS, 2018.
[16] Schwendicke F, Elhennawy K, Paris S, et al. Deep learning for caries lesion detection in near-infrared light transillumination images: a pilot study[J]. J Dent, 2020, 92: 103260.
[17] Sonavane A, Yadav R, Khamparia A. Dental cavity classification of using convolutional neural network[J]. IOP Conf Ser: Mater Sci Eng, 2021, 1022(1): 012116.
[18] Prajapati SA, Nagaraj R, Mitra S. Classification of dental diseases using CNN and transfer learning[C]//2017 5th International Symposium on Computatio-nal and Business Intelligence (ISCBI). Dubail: IEEE, 2017: 70-74.
[19] Bui TH, Hamamoto K, Paing MP. Deep fusion feature extraction for caries detection on dental pa-noramic radiographs[J]. Appl Sci, 2021, 11(5): 2005.
[20] Haghanifar A, Majdabadi MM, Haghanifar S, et al. PaXNet: tooth segmentation and dental caries detection in panoramic X-ray using ensemble transfer learning and capsule classifier[J]. Multimed Tools Appl, 2023, 82(18): 27659-27679.
[21] Imak A, Celebi A, Siddique K, et al. Dental caries detection using score-based multi-input deep convolutional neural network[J]. IEEE Access, 2022, 10: 18320-18329.
[22] Zhou XJ, Feng XO, Li QM, et al. Position weighted convolutional neural network for unbalanced children caries diagnosis[J]. IEEE Access, 2023, 11: 77034-77044.
[23] Han K, Wang YH, Chen HT, et al. A survey on vision transformer[J]. IEEE Trans Pattern Anal Mach Intell, 2023, 45(1): 87-110.
[24] Zhou XJ, Yu GX, Yin QY, et al. Tooth type enhanced transformer for children caries diagnosis on dental panoramic radiographs[J]. Diagnostics, 2023, 13(4): 689.
[25] Esmaeilyfard R, Bonyadifard H, Paknahad M. Dental caries detection and classification in CBCT ima-ges using deep learning[J]. Int Dent J, 2024, 74(2): 328-334.
[26] 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.
[27] Cantu AG, Gehrung S, Krois J, et al. Detecting ca-ries lesions of different radiographic extension on bitewings using deep learning[J]. J Dent, 2020, 100: 103425.
[28] Siddique N, Paheding S, Elkin CP, et al. U-net and its variants for medical image segmentation: a review of theory and applications[J]. IEEE Access, 2021, 9: 82031-82057.
[29] Khan HA, Haider MA, Ansari HA, et al. Automated feature detection in dental periapical radiographs by using deep learning[J]. Oral Surg Oral Med Oral Pathol Oral Radiol, 2021, 131(6): 711-720.
[30] Dayı B, Üzen H, Çiçek İB, et al. A novel deep lear-ning-based approach for segmentation of different type caries lesions on panoramic radiographs[J]. Dia-gnostics, 2023, 13(2): 202.
[31] Zhu HH, Cao Z, Lian LY, et al. CariesNet: a deep learning approach for segmentation of multi-stage caries lesion from oral panoramic X-ray image[J]. Neural Comput Applic, 2023, 35(22): 16051-16059.
[32] Amasya H, Alkhader M, Serindere G, et al. Evaluation of a decision support system developed with deep learning approach for detecting dental caries with cone-beam computed tomography imaging[J]. Diagnostics, 2023, 13(22): 3471.
[33] 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.
Pakezhati·Seyiti, Wang TM, Xu ZN, et al. Preliminary study of common oral diseases recognition in oral pantomography with deep learning[J]. J Oral Sci Res, 2021, 37(9): 845-849.
[34] Zhou XJ, Yu GX, Yin QY, et al. Context aware convolutional neural network for children caries diagnosis on dental panoramic radiographs[J]. Comput Math Methods Med, 2022, 2022: 6029245.
[35] 周飞燕, 金林鹏, 董军. 卷积神经网络研究综述[J]. 计算机学报, 2017, 40(6): 1229-1251.
Zhou FY, Jin LP, Dong J. Review of convolutional neural network[J]. Chin J Comput, 2017, 40(6): 1229-1251.
[36] Ying S, Wang B, Zhu H, et al. Caries segmentation on tooth X-ray images with a deep network[J]. J Dent, 2022, 119: 104076.
[37] Zhang Y, Ye F, Chen L, et al. Children’s dental pa-noramic radiographs dataset for caries segmentation and dental disease detection[J]. Sci Data, 2023, 10: 380.
[38] Mello Román JC, Fretes VR, Adorno CG, et al. Pa-noramic dental radiography image enhancement u-sing multiscale mathematical morphology[J]. Sensors, 2021, 21(9): 3110
[39] Abdi AH, Kasaei S, Mehdizadeh M. Automatic segmentation of mandible in panoramic X-ray[J]. J Med Imaging (Bellingham), 2015, 2(4): 044003.
[40] Hamamci IE, Er S, Simsar E, et al. DENTEX: an abnormal tooth detection with dental enumeration and diagnosis benchmark for panoramic X-rays[J]. ar-Xiv, 2023: arXiv:.
[41] Krois J, Garcia Cantu A, Chaurasia A, et al. Generali-zability of deep learning models for dental image analysis[J]. Sci Rep, 2021, 11(1): 6102.
[42] Bodria F, Giannotti F, Guidotti R, et al. Benchmar-king and survey of explanation methods for black box models[J]. arXiv, 2021: arXiv:.
[43] 俞益洲, 石德君, 马杰超, 等. 人工智能在医学影像分析中的应用进展[J]. 中国医学影像技术, 2019, 35(12): 1808-1812.
Yu YZ, Shi DJ, Ma JC, et al. Advances in application of artificial intelligence in medical image analysis[J]. Chin J Med Imag Technol, 2019, 35(12): 1808-1812.
[44] Wu TY, He SZ, Liu JP, et al. A brief overview of ChatGPT: the history, status quo and potential future development[J]. IEEE/CAA J Autom Sinica, 2023, 10(5): 1122-1136.
[45] Huang H, Zheng O, Wang D, et al. ChatGPT for shaping the future of dentistry: the potential of multi-modal large language model[J]. Int J Oral Sci, 2023, 15(1): 29.
[1] 林超英,张岚,黄定明. 人工智能在根管治疗中的研究进展[J]. 国际口腔医学杂志, 2025, 52(5): 572-578.
[2] 汪云毅,朱珠,张峰. 人工智能在头影测量自动定点算法上的研究进展[J]. 国际口腔医学杂志, 2024, 51(5): 630-641.
[3] 陈新月,潘晓予,杨燕,贾媛媛,陈亮. 卷积神经网络在牙体牙髓病学中的应用进展[J]. 国际口腔医学杂志, 2024, 51(4): 483-488.
[4] 靳淑凤，代晓华，姚晖，姚晓天，李燕妮，万木森，连小丽，梁雨. 光学相干断层成像术对人牙窝沟釉质早期龋变深度的测量[J]. 国际口腔医学杂志, 2010, 37(6): 637-640.
[5] 雷芳. 直接数字式摄影系统在牙体牙髓病临床的应用[J]. 国际口腔医学杂志, 2000, 27(03): -.
Viewed
Full text


Abstract

Cited
  Shared   
  Discussed   
[1] 张磊涛 范亚伟 南欣荣. 影响血管化游离自体骨移植术后肥大的因素[J]. 国际口腔医学杂志, 2003, 30(05): 370 -372 .