国际口腔医学杂志

国际口腔医学杂志 ›› 2026, Vol. 53 ›› Issue (5): 631-638.doi: 10.7518/gjkq.2026616

• 数字化口腔医学专栏 • 上一篇    

人工智能在牙体牙髓疾病诊断中的应用

蔡宛伶(),郑庆华,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 口腔疾病国家临床医学研究中心 四川大学华西口腔医院牙体牙髓病科 成都 610041
  • 收稿日期:2025-04-22 修回日期:2026-02-26 出版日期:2026-09-01 发布日期:2026-08-28
  • 通讯作者: 黄定明
  • 作者简介:蔡宛伶，硕士，Email：cwl18048406627@163.com
  • 基金资助:
    成都市重点研发支撑计划(2024-YF05-02478-SN);四川省科技计划项目(2024NSFSC0593)

Applications of artificial intelligence in the diagnosis of endodontal diseases

Wanling Cai(),Qinghua Zheng,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:2025-04-22 Revised:2026-02-26 Online:2026-09-01 Published:2026-08-28
  • Contact: Dingming Huang
  • Supported by:
    Chengdu Key R&D Support Plan(2024-YF05-02478-SN);Sichuan Province Science and Technology Program(2024NSFSC0593)

RichHTML

2

PDF (PC)

16

摘要：

随着人工智能技术的飞速发展，其在牙体牙髓病学领域的应用稳步增长。本研究对人工智能在牙体牙髓疾病（龋病、牙体硬组织非龋性疾病、根尖周炎等）诊断方面的最新进展进行综述，并探讨人工智能辅助诊断的优势，以及数据集的质量、人工智能的透明度及潜在的伦理困境所带来的局限性和挑战。

关键词: 人工智能, 牙体牙髓疾病, 诊断

Abstract:

In recent years, the rapid development of artificial intelligence (AI) technology has led to a steady rise in its application in the field of endodontics. This review focuses on evaluating the latest advancements of AI in the diagnosis of endodontic diseases, including caries, non-carious diseases of dental hard tissues, and periapical lesions. It discusses the advantages of AI-assisted diagnosis and reviews the limitations and challenges posed by dataset quality, transparency of AI, and potential ethical dilemmas. In the near future, AI is expected to significantly influence the daily workflow, diagnosis, and treatment strategies of dental and pulp diseases.

Key words: artificial intelligence, endodontal diseases, diagnosis

中图分类号: 

  • R781.05

表 1

基于口内照片的AI在龋病诊断中的相关研究"

作者年份成像设备样本量学习模型评价结果
Khan等[11]2022多类型609CNNDental-Net、VGG-16、MobileNetV2、InceptionV3、ResNet-18的准确率分别为91.09%、87.76%、84.55%、82.65%、86.78%
Park等[12]2022口内摄像机2 348CNNResNet-18、Faster R-CNN的准确率分别为81.3%、89.0%
Kühnisch等[13]2022专业摄像机2 417CNN敏感度89.6%，精确度93.6%，特异性94.3%，准确率92.5%
Sonavane等[14]2021-74CNN准确率71.4%
Liu等[15]2020口内摄像机12 600CNN敏感度97.7%，精确度91.3%，特异性92.9%，准确率95.0%

表 2

基于二维X线片的AI在龋病诊断中的相关研究"

作者年份样本量X线片类型学习模型评价结果
Liu等[18]20244 278根尖片CNN敏感度89%，特异性89%，准确率88%
Dhanak等[19]2024100咬翼片ANN敏感度75%，精确度85%
Estai等[20]20222 468咬翼片CNN敏感度89%，特异性86%，准确率87%
Bayraktar等[24]20221 000咬翼片CNN敏感度72%，特异性98%，准确率94.6%
Chen等[25]2022978咬翼片CNN敏感度72%，特异性93%，准确率87%
Zhu等[21]20221 159全景片CNN敏感度86%，精确度94.09%，准确率93.61%
Chen等[23]20221 100全景片CNN敏感度82.3%，精确度74.1%，F1分数78.0%
Vinayahalingam等[22]2021400全景片CNN敏感度86%，特异性88%，准确率87%

表 3

基于放射学影像的AI在根尖周炎诊断中的相关研究"

作者年份成像类型学习模型样本量评价结果
Shafi等[41]2023根尖片CNN534准确率98%，精确度76%，敏感度75%
Li等[42]2021根尖片CNN460准确率93%，特异性90%，敏感度95%，精确度92%
Chen等[44]2021根尖片CNN2 900精确度52%，敏感度52%
Calazans等[43]2022CBCTCNN885准确率70%，精确度76%，特异性76%
Kirnbauer等[45]2022CBCTCNN144敏感度97%，特异性88%
Endres等[46]2020全景片CNN3 099阳性预测值67%，F1分数58%，曲线下面积60%
Ekert等[47]2019全景片CNN2 579曲线下面积85%，敏感度65%，特异性87%
[1] Liu SD, Russo C, Suero Molina E, et al. Artificial intelligence methods[M]//Advances in experimental medicine and biology. Cham: Springer Nature Switzerland, 2024: 21-38.
[2] Choi RY, Coyner AS, Kalpathy-Cramer J, et al. Introduction to machine learning, neural networks, and deep learning[J]. Transl Vis Sci Technol, 2020, 9(2): 14.
[3] Kaul V, Enslin S, Gross SA. History of artificial intelligence in medicine[J]. Gastrointest Endosc, 2020, 92(4): 807-812.
[4] D'Angelo M, Zanza A, di Nardo D, et al. Innovations in diagnostic imaging in oral and maxillofacial diseases[J]. Diagnostics, 2022, 12(2): 536.
[5] Shah N, Bansal N, Logani A. Recent advances in imaging technologies in dentistry[J]. World J Radiol, 2014, 6(10): 794-807
[6] De Angelis F, Pranno N, Franchina A, et al. Artificial intelligence: a new diagnostic software in dentistry: a preliminary performance diagnostic study[J]. Int J Environ Res Public Health, 2022, 19(3): 1728.
[7] Bonny T, Al Nassan W, Obaideen K, et al. Contemporary role and applications of artificial intelligence in dentistry[J]. F1000Res, 2023, 12: 1179.
[8] Semenov K, Stepanova S. Modern ideas about the nature of dental diseases[J]. Grail Sci, 2023(27): 514-519.
[9] Patil S, Albogami S, Hosmani J, et al. Artificial intelligence in the diagnosis of oral diseases: applications and pitfalls[J]. Diagnostics, 2022, 12(5): 1029.
[10] Putra RH, Doi C, Yoda N, et al. Current applications and development of artificial intelligence for digital dental radiography[J]. Dentomaxillofacial Radiol, 2022, 51: 20210197.
[11] Khan MAH, Giri PS, Jothi JAA. Detection of cavities from oral images using convolutional neural networks[C]//2022 International Conference on Electrical, Computer and Energy Technologies (ICECET). Prague: IEEE, 2022.
[12] Park EY, Cho H, Kang S, et al. Caries detection with tooth surface segmentation on intraoral photographic images using deep learning[J]. BMC Oral Heal, 2022, 22: 573.
[13] Kühnisch J, Meyer O, Hesenius M, et al. Caries detection on intraoral images using artificial intelligen-ce[J]. J Dent Res, 2022, 101(2): 158-165.
[14] 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.
[15] Liu LZ, Xu JW, Huan YX, et al. A smart dental health-IoT platform based on intelligent hardware, deep learning, and mobile terminal[J]. IEEE J Bio-med Health Inform, 2020, 24(3): 898-906.
[16] Lindfors N, Ekestubbe A, Frisk F, et al. Is cone-beam computed tomography (CBCT) an alternative to plain radiography in assessments of dental disea-se? A study of method agreement in a medically compromised patient population[J]. Clin Oral Invest, 2024, 28(2): 127.
[17] Al Saffan AD. Current approaches to diagnosis of early proximal carious lesion: a literature review[J]. Cureus, 2023, 15(8): e43489
[18] Liu YL, Xia K, Cen YY, et al. Artificial intelligence for caries detection: a novel diagnostic tool using deep learning algorithms[J]. Oral Radiol, 2024, 40(3): 375-384.
[19] Dhanak N, Chougule VT, Nalluri K, et al. Artificial intelligence enabled smart phone app for real-time caries detection on bitewing radiographs[J]. Bioinformation, 2024, 20(3): 243-247.
[20] Estai M, Tennant M, Gebauer D, et al. Evaluation of a deep learning system for automatic detection of proximal surface dental caries on bitewing radiogra-phs[J]. Oral Surg Oral Med Oral Pathol Oral Radiol, 2022, 134(2): 262-270.
[21] 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 Appl, 2022: 1-9.
[22] 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.
[23] Chen QG, Huang JC, Zhu HH, et al. Automatic and visualized grading of dental caries using deep lear-ning on panoramic radiographs[J]. Multimed Tools Appl, 2023, 82(15): 23709-23734.
[24] Bayraktar Y, Ayan E. Diagnosis of interproximal ca-ries lesions with deep convolutional neural network in digital bitewing radiographs[J]. Clin Oral Invest, 2022, 26(1): 623-632.
[25] Chen XT, Guo JC, Ye JX, et al. Detection of proximal caries lesions on bitewing radiographs using deep learning method[J]. Caries Res, 2023, 56(5/6): 455-463.
[26] Krzyżostaniak J, Surdacka A, Kulczyk T, et al. Diagnostic accuracy of cone beam computed tomography compared with intraoral radiography for the detection of noncavitated occlusal carious lesions[J]. Caries Res, 2014, 48(5): 461-466.
[27] 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.
[28] 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.
[29] Marinova-Takorova M, Panov V, Anastasova R. Effectiveness of near-infrared transillumination in early caries diagnosis[J]. Biotechnol Biotechnol Equip, 2016, 30(6): 1207-1211.
[30] Topoliceanu C, Ghiorghe CA, Toma V, et al. Assessment of near-infrared (780 nm) light transillumination accuracy for detection of incipient caries lesions[J]. Rev Chim, 2019, 70(4): 1333-1336.
[31] Holtkamp A, Elhennawy K, Cejudo Grano de Oro JE, et al. Generalizability of deep learning models for caries detection in near-infrared light transillumination images[J]. J Clin Med, 2021, 10(5): 961.
[32] 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.
[33] 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.
[34] Guo JC, Wu YY, Chen LZ, et al. Automatic detection of cracks in cracked tooth based on binary classification convolutional neural networks[J]. Appl Bionics Biomech, 2022: 9333406.
[35] Xie ZW, Lu QL, Guo JC, et al. Semantic segmentation for tooth cracks using improved DeepLabv3+ model[J]. Heliyon, 2024, 10(4): e25892.
[36] Reddy TVK, Chellapandian K, Priyadharshini V, et al. Recent trends in vertical root fracture: a review[J]. Int J Health Sci, 2022, 6(S2): 11467-11475.
[37] Patel S, Bhuva B, Bose R. Present status and future directions: vertical root fractures in root filled teeth[J]. Int Endod J, 2022, 55(S3): 804-826.
[38] Khasnis S, Kidiyoor K, Patil A, et al. Vertical root fractures and their management[J]. J Conserv Dent, 2014, 17(2): 103.
[39] Hu ZY, Cao DT, Hu YN, et al. Diagnosis of in vivo vertical root fracture using deep learning on cone-beam CT images[J]. BMC Oral Heal, 2022, 22: 382.
[40] Fukuda M, Inamoto K, Shibata N, et al. Evaluation of an artificial intelligence system for detecting vertical root fracture on panoramic radiography[J]. Oral Radiol, 2020, 36(4): 337-343.
[41] Shafi I, Sajad M, Fatima A, et al. Teeth lesion detection using deep learning and the internet of things post-COVID-19[J]. Sensors, 2023, 23(15): 6837.
[42] Li CW, Lin SY, Chou HS, et al. Detection of dental apical lesions using CNNs on periapical radiograph[J]. Sensors, 2021, 21(21): 7049.
[43] Calazans MAA, Ferreira FABS, ML-MG Alcoforado, et al. Automatic classification system for peria-pical lesions in cone-beam computed tomography[J]. Sensors, 2022, 22(17): 6481.
[44] Chen H, Li H, Zhao YJ, et al. Dental disease detection on periapical radiographs based on deep convolutional neural networks[J]. Int J Comput Assist Radiol Surg, 2021, 16(4): 649-661.
[45] Kirnbauer B, Hadzic A, Jakse N, et al. Automatic detection of periapical osteolytic lesions on cone-beam computed tomography using deep convolutional neuronal networks[J]. J Endod, 2022, 48(11): 1434-1440.
[46] Endres MG, Hillen F, Salloumis M, et al. Development of a deep learning algorithm for periapical di-sease detection in dental radiographs[J]. Diagnostics, 2020, 10(6): 430.
[47] Ekert T, Krois J, Meinhold L, et al. Deep learning for the radiographic detection of apical lesions[J]. J Endod, 2019, 45(7): 917-922.e5.
[48] Ricucci D, Rôças IN, Hernández S, et al. “True” versus “bay” apical cysts: clinical, radiographic, histopathologic, and histobacteriologic features[J]. J Endod, 2020, 46(9): 1217-1227.
[49] Setzer FC, Kratchman SI. Present status and future directions: surgical endodontics[J]. Int Endod J, 2022, 55(S4): 1020-1058.
[50] Simon JHS, Enciso R, Malfaz JM, et al. Differential diagnosis of large periapical lesions using cone-beam computed tomography measurements and bio-psy[J]. J Endod, 2006, 32(9): 833-837.
[51] Okada K, Rysavy S, Flores A, et al. Noninvasive differential diagnosis of dental periapical lesions in cone-beam CT scans[J]. Med Phys, 2015, 42(4): 1653-1665.
[52] Ver Berne J, Saadi SB, Politis C, et al. A deep lear-ning approach for radiological detection and classification of radicular cysts and periapical granulomas[J]. J Dent, 2023, 135: 104581.
[53] Wang JJ. The power of AI-assisted diagnosis[J]. EAI Endorsed Trans e-Learn, 2023, 8(4): e3.
[54] Müller A, Mertens SM, Göstemeyer G, et al. Bar-riers and enablers for artificial intelligence in dental diagnostics: a qualitative study[J]. J Clin Med, 2021, 10(8): 1612.
[55] Shan T, Tay FR, Gu L. Application of artificial intelligence in dentistry[J]. J Dent Res, 2021, 100(3): 232-244.
[56] Schwendicke F, Samek W, Krois J. Artificial intelligence in dentistry: chances and challenges[J]. J Dent Res, 2020, 99(7): 769-774.
[57] Mahesh Batra A, Reche A. A new era of dental care: harnessing artificial intelligence for better diagnosis and treatment[J]. Cureus, 2023, 15(11): e49319.
[58] Huang JY, Farpour N, Yang BJ, et al. Uncertainty-based active learning by Bayesian U-net for multi-label cone-beam CT segmentation[J]. J Endod, 2024, 50(2): 220-228.
[59] Khanagar SB, Alfadley A, Alfouzan K, et al. Deve-lopments and performance of artificial intelligence models designed for application in endodontics: a systematic review[J]. Diagnostics, 2023, 13(3): 414.
[60] Goncharuk-Khomyn M, Noenko I, Cavalcanti AL, et al. Artificial intelligence in endodontics: relevant trends and practical perspectives[J]. Ukrainian Dent J, 2023, 2(1): 96-101.
[61] Singh S, Asthana G. Artificial intelligence—a futu-ristic tool for advanced endodontics[J]. J Conserv Dent Endod, 2024, 27(5): 447-448.
[62] Kundu S. AI in medicine must be explainable[J]. Nat Med, 2021, 27(8): 1328.
[63] Shah H. Algorithmic accountability[J]. Phil Trans R Soc A, 2018, 376(2128): 20170362.
[1] 洪曼瑶,郭米嘉,周婷,邓子龙. 釉珠研究现状与临床诊疗策略[J]. 国际口腔医学杂志, 2026, 53(2): 230-238.
[2] 张琳涵,汤亚玲. 拉曼光谱技术在口腔鳞状细胞癌和口腔潜在恶性疾病诊断和治疗中的应用进展[J]. 国际口腔医学杂志, 2026, 53(1): 107-115.
[3] 黄超,石佳,潘珮玥,于乐,唐甜. 人工智能时代下口腔医学教师的角色重构[J]. 国际口腔医学杂志, 2026, 53(1): 134-142.
[4] 张俭, 白雪, 何小倩, 葛振林. 多模态数据融合技术在口腔正畸领域的应用及研究进展[J]. 国际口腔医学杂志, 2025, 52(6): 764-770.
[5] 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.
[6] 周小洁,侯本祥. 基于深度学习技术诊断龋病方法的研究进展[J]. 国际口腔医学杂志, 2025, 52(5): 579-585.
[7] 黄美畅,蒋鸿杰,汤亚玲,姚莉洪. 锥形束CT及免疫组织化学染色在根尖周囊肿诊断与鉴别诊断中的应用[J]. 国际口腔医学杂志, 2025, 52(4): 490-497.
[8] 于寰,康健. 牙周内镜在口腔临床诊疗中的应用进展[J]. 国际口腔医学杂志, 2025, 52(4): 544-551.
[9] 孙睿哲,倪前伟,高瞻. 数字化技术在颌面部恶性肿瘤近距离照射治疗中的应用进展[J]. 国际口腔医学杂志, 2025, 52(1): 18-24.
[10] 符天宇,张雪,王思琪,郭程祎,董晓宇,程亚鹏,李锐. 碳点在口腔颌面部恶性肿瘤的诊断及治疗中的应用[J]. 国际口腔医学杂志, 2025, 52(1): 25-33.
[11] 蒋思鑫,施雯锦,罗小波,陈谦明. 儿童病毒性口炎诊断及治疗的研究进展[J]. 国际口腔医学杂志, 2024, 51(5): 519-531.
[12] 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.
[13] 张宇,贾志宇,唐慧芳,张志勇,李文静,田松波. 免疫球蛋白G4相关性唾液腺炎临床特点及诊疗进展[J]. 国际口腔医学杂志, 2024, 51(2): 193-200.
[14] 刘洋,尹德强. 关于颌位调整方法的思考和改进[J]. 国际口腔医学杂志, 2023, 50(5): 499-505.
[15] 李奕君, 徐子昂, 李一. 前哨淋巴结在头颈部鳞状细胞癌检测的应用进展[J]. 国际口腔医学杂志, 2023, 50(5): 521-527.
Viewed
Full text


Abstract

Cited
  Shared   
  Discussed   
No Suggested Reading articles found!