国际口腔医学杂志

国际口腔医学杂志 ›› 2025, Vol. 52 ›› Issue (5): 572-578.doi: 10.7518/gjkq.2025087

• 人工智能专栏 • 上一篇    下一篇

人工智能在根管治疗中的研究进展

林超英(),张岚,黄定明()   

  1. 口腔疾病防治全国重点实验室 国家口腔医学中心 国家口腔疾病临床医学研究中心四川大学华西口腔医院牙体牙髓病科 成都 610041
  • 收稿日期:2024-08-30 修回日期:2024-11-11 出版日期:2025-09-01 发布日期:2025-08-27
  • 通讯作者: 黄定明
  • 作者简介:林超英，硕士，Email：cylin_2018@163.com
  • 基金资助:
    四川大学华西口腔医院探索与研发项目(LCYJ-MS-202304)

Research progress of artificial intelligence in root canal therapy

Chaoying Lin(),Lan Zhang,Dingming Huang()   

  1. State Key Laboratory of Oral Diseases & National Center for Stomatology & National Clinical Research Center for Oral Diseases & Dept. of Cariology and Endodontics, West China Hospital of Stomatology, Sichuan University, Chengdu 610041, China
  • Received:2024-08-30 Revised:2024-11-11 Online:2025-09-01 Published:2025-08-27
  • Contact: Dingming Huang
  • Supported by:
    Research and Develop Program, West China Hospital of Stomatology, Sichuan University(LCYJ-MS-202304)

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摘要：

根管治疗术是牙髓根尖周病最有效的治疗方法之一，可控制牙髓根尖周炎症，促进病变愈合，保留天然牙。人工智能技术发展迅速，可高效且准确地获取和处理医学影像和临床医学资料，具有促进口腔医学领域发展的潜能。本文围绕根管治疗术前根管形态识别、治疗难度评估和辅助根管再治疗的决策，术中工作长度确定和试尖片、根充片解读，术后预后预测三方面，简要介绍了人工智能在根管治疗过程中的研究进展，给未来的发展提供一定的参考。

关键词: 人工智能, 根管治疗, 深度学习

Abstract:

Root canal therapy is one of the most effective treatments for pulp and periapical diseases, which is capable of controlling pulp and periapical inflammation, promoting apical lesion healing, and preserving natural teeth. Artificial intelligence (AI) can process medical images and clinical data efficiently and accurately, presenting significant potential for promoting the development of stomatology. This article briefly introduces the research progress of AI in root canal therapy, focusing on the following aspects: pre-treatment root canal morphology recognition, case difficulty assessment, decision-making for root canal retreatment, intraoperative working length determination interpretation of root canal filling films, and postoperative prognosis prediction. This article provides a certain reference for future development.

Key words: artificial intelligence, root canal therapy, deep learning

中图分类号: 

  • R781.05
[1] 姜葳, 梁景平. 牙髓根尖周病的诊断技术进展概述[J]. 中华口腔医学杂志, 2022, 57(3): 227-232.
Jiang W, Liang JP. Overview of technical advances in the diagnosis of pulp and periapical diseases[J]. Chin J Stomatol, 2022, 57(3): 227-232.
[2] López-Valverde I, Vignoletti F, Vignoletti G, et al. Long-term tooth survival and success following primary root canal treatment: a 5- to 37-year retrospective observation[J]. Clin Oral Investig, 2023, 27(6): 3233-3244.
[3] Duan W, Chen YF, Zhang Q, et al. Refined tooth and pulp segmentation using U-Net in CBCT image[J]. Dentomaxillofac Radiol, 2021, 50(6): 20200251.
[4] Tan MH, Cui ZM, Zhong T, et al. A progressive framework for tooth and substructure segmentation from cone-beam CT images[J]. Comput Biol Med, 2024, 169: 107839.
[5] Lin X, Fu YJ, Ren GQ, et al. Micro-computed tomography-guided artificial intelligence for pulp ca-vity and tooth segmentation on cone-beam computed tomography[J]. J Endod, 2021, 47(12): 1933-1941.
[6] Wang YW, Xia WJ, Yan ZN, et al. Root canal treatment planning by automatic tooth and root canal segmentation in dental CBCT with deep multi-task feature learning[J]. Med Image Anal, 2023, 85: 102750.
[7] Ali Ahmad I, Al-Jadaa A. Three root canals in the mesiobuccal root of maxillary molars: case reports and literature review[J]. J Endod, 2014, 40(12): 2087-2094.
[8] Hiraiwa T, Ariji Y, Fukuda M, et al. A deep-learning artificial intelligence system for assessment of root morphology of the mandibular first molar on pano-ramic radiography[J]. Dentomaxillofac Radiol, 2019, 48(3): 20180218.
[9] Alotaibi BB, Khan KI, Javed MQ, et al. Relationship between apical periodontitis and missed canals in mesio-buccal roots of maxillary molars: CBCT study[J]. J Taibah Univ Med Sci, 2024, 19(1): 18-27.
[10] Parker J, Mol A, Rivera EM, et al. CBCT uses in clinical endodontics: the effect of CBCT on the abi-lity to locate MB2 canals in maxillary molars[J]. Int Endod J, 2017, 50(12): 1109-1115.
[11] Studebaker B, Hollender L, Mancl L, et al. The incidence of second mesiobuccal canals located in ma-xillary molars with the aid of cone-beam computed tomography[J]. J Endod, 2018, 44(4): 565-570.
[12] Albitar L, Zhao TY, Huang C, et al. Artificial intelligence (AI) for detection and localization of unobturated second mesial buccal (MB2) canals in cone-beam computed tomography (CBCT)[J]. Diagnostics, 2022, 12(12): 3214.
[13] Duman ŞB, Çelik Özen D, Bayrakdar IŞ, et al. Se-cond mesiobuccal canal segmentation with YOLOv5 architecture using cone beam computed tomography images[J]. Odontology, 2024, 112(2): 552-561.
[14] Kato A, Ziegler A, Higuchi N, et al. Aetiology, incidence and morphology of the C-shaped root canal system and its impact on clinical endodontics[J]. Int Endod J, 2014, 47(11): 1012-1033.
[15] Jeon SJ, Yun JP, Yeom HG, et al. Deep-learning for predicting C-shaped canals in mandibular second molars on panoramic radiographs[J]. Dentomaxillofac Radiol, 2021, 50(5): 20200513.
[16] Zhang LJ, Xu F, Li Y, et al. A lightweight convolutional neural network model with receptive field block for C-shaped root canal detection in mandibular second molars[J]. Sci Rep, 2022, 12(1): 17373.
[17] Yang SJ, Kim KD, Kise Y, et al. External validation of the effect of the combined use of object detection for the classification of the C-shaped canal configuration of the mandibular second molar in panoramic radiographs: a multicenter study[J]. J Endod, 2024, 50(5): 627-636.
[18] Sherwood AA, Sherwood AI, Setzer FC, et al. A deep learning approach to segment and classify C-shaped canal morphologies in mandibular second molars using cone-beam computed tomography[J]. J Endod, 2021, 47(12): 1907-1916.
[19] Yang SJ, Lee H, Jang B, et al. Development and va-lidation of a visually explainable deep learning mo-del for classification of C-shaped canals of the mandibular second molars in periapical and panoramic dental radiographs[J]. J Endod, 2022, 48(7): 914-921.
[20] Yuce F, Öziç MÜ, Tassoker M. Detection of pulpal calcifications on bite-wing radiographs using deep learning[J]. Clin Oral Investig, 2023, 27(6): 2679-2689.
[21] Altındağ A, Bahrilli S, Çelik Ö, et al. The detection of pulp stones with automatic deep learning in pano-ramic radiographies: an AI pilot study[J]. Diagnostics, 2024, 14(9): 890.
[22] 温佳欢, 傅裕杰, 任根强, 等. 基于深度学习的点云补全网络修复上颌磨牙钙化根管的研究[J]. 口腔医学研究, 2023, 39(5): 455-459.
Wen JH, Fu YJ, Ren GQ, et al. Deep learning-based point cloud completion network for restoration of calcified root canals in maxillary molars[J]. J Oral Sci Res, 2023, 39(5): 455-459.
[23] Essam O, Boyle EL, Whitworth JM, et al. The en-dodontic complexity assessment tool (E-CAT): a di-gital form for assessing root canal treatment case difficulty[J]. Int Endod J, 2021, 54(7): 1189-1199.
[24] Mallishery S, Chhatpar P, Banga KS, et al. The precision of case difficulty and referral decisions: an innovative automated approach[J]. Clin Oral Investig, 2020, 24(6): 1909-1915.
[25] Karkehabadi H, Khoshbin E, Ghasemi N, et al. Deep learning for determining the difficulty of en-dodontic treatment: a pilot study[J]. BMC Oral Health, 2024, 24(1): 574.
[26] Campo L, Aliaga IJ, de Paz JF, et al. Retreatment predictions in odontology by means of CBR Systems[J]. Comput Intell Neurosci, 2016, 2016: 7485250.
[27] 黄定明, 谭学莲, 张岚, 等. 根管治疗工作长度确定之惑及解决之道[J]. 华西口腔医学杂志, 2016, 34(2): 109-114.
Huang DM, Tan XL, Zhang L, et al. Confusion and solution for root canal working length determination[J]. West China J Stomatol, 2016, 34(2): 109-114.
[28] Serna-Peña G, Gomes-Azevedo S, Flores-Treviño J, et al. In vivo evaluation of 3 electronic apex locators: root ZX mini, apex ID, and propex pixi[J]. J Endod, 2020, 46(2): 158-161.
[29] Qiao XY, Zhang Z, Chen X. Multifrequency impe-dance method based on neural network for root canal length measurement[J]. Appl Sci, 2020, 10(21): 7430.
[30] Saghiri MA, Garcia-Godoy F, Gutmann JL, et al. The reliability of artificial neural network in loca-ting minor apical foramen: a cadaver study[J]. J Endod, 2012, 38(8): 1130-1134.
[31] Saghiri MA, Asgar K, Boukani KK, et al. A new approach for locating the minor apical foramen using an artificial neural network[J]. Int Endod J, 2012, 45(3): 257-265.
[32] Liang YH, Jiang L, Chen C, et al. The validity of cone-beam computed tomography in measuring root canal length using a gold standard[J]. J Endod, 2013, 39(12): 1607-1610.
[33] Çelik B, Genç MZ, Çelik ME. Evaluation of root canal filling length on periapical radiograph using artificial intelligence[J]. Oral Radiol, 2025, 41(1): 102-110.
[34] Li YX, Zeng GD, Zhang YF, et al. AGMB-transformer: anatomy-guided multi-branch transformer network for automated evaluation of root canal the-rapy[J]. IEEE J Biomed Health Inform, 2022, 26(4): 1684-1695.
[35] Herbst CS, Schwendicke F, Krois J, et al. Association between patient-, tooth- and treatment-level factors and root canal treatment failure: a retrospective longitudinal and machine learning study[J]. J Dent, 2022, 117: 103937.
[36] Herbst SR, Herbst CS, Schwendicke F. Preoperative risk assessment does not allow to predict root filling length using machine learning: a longitudinal study[J]. J Dent, 2023, 128: 104378.
[37] Bennasar C, García I, Gonzalez-Cid Y, et al. Second opinion for non-surgical root canal treatment prognosis using machine learning models[J]. Diagnostics, 2023, 13(17): 2742.
[38] Lee J, Seo H, Choi YJ, et al. An endodontic forecas-ting model based on the analysis of preoperative dental radiographs: a pilot study on an endodontic predictive deep neural network[J]. J Endod, 2023, 49(6): 710-719.
[39] Van Nieuwenhuysen JP, D’Hoore W, Leprince JG. What ultimately matters in root canal treatment success and tooth preservation: a 25-year cohort study[J]. Int Endod J, 2023, 56(5): 544-557.
[40] Pak JG, White SN. Pain prevalence and severity before, during, and after root canal treatment: a systematic review[J]. J Endod, 2011, 37(4): 429-438.
[41] Gao X, Xin X, Li Z, et al. Predicting postoperative pain following root canal treatment by using artificial neural network evaluation[J]. Sci Rep, 2021, 11(1): 17243.
[42] Chang WT, Huang HY, Lee TM, et al. Predicting root fracture after root canal treatment and crown installation using deep learning[J]. J Dent Sci, 2024, 19(1): 587-593.
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