智能化时代下的口腔黏膜病学——人工智能应用研究的现状与展望

doi:10.13701/j.cnki.kqyxyj.2025.01.001

口腔医学研究 ›› 2025, Vol. 41 ›› Issue (1): 1-6.DOI: 10.13701/j.cnki.kqyxyj.2025.01.001

• 综述 • 下一篇

智能化时代下的口腔黏膜病学——人工智能应用研究的现状与展望

杨青默, 魏攀^*, 华红^*

北京大学口腔医院口腔黏膜科国家口腔疾病临床医学研究中心口腔生物材料和数字诊疗装备国家工程研究中心口腔数字医学北京重点实验室北京 100081

收稿日期:2024-08-15 出版日期:2025-01-28 发布日期:2025-01-24
通讯作者: * 魏攀,E-mail:dent_wei@163.com；华红,E-mail:honghua1968@aliyun.com
作者简介:杨青默(2000~ ),男,辽宁阜新人,博士在读,研究方向:口腔黏膜病学。
基金资助:
国家重点研发计划课题(编号:2023YFC3605603)；北京市自然联合基金L242115(编号:30307472420)；海淀区卫生健康发展科研培育计划(编号:HP2023-12-509002)

Oral Mucosal Diseases in the Era of Intelligence: Current Status and Prospects of Artificial Intelligence Applications in Research

YANG Qingmo, WEI Pan^*, HUA Hong^*

Department of Oral Mucosa, Peking University Hospital of Stomatology, National Clinical Research Center for Oral Diseases, National Engineering Research Center of Oral Biomaterials and Digital Medical Devices, Beijing Key Laboratory of Oral Digital Medicine, Beijing 100081, China

Received:2024-08-15 Online:2025-01-28 Published:2025-01-24

摘要/Abstract

摘要： 随着人工智能(artificial intelligence, AI)技术的发展,其在医学领域的融合应用受到广泛关注。医学人工智能在影像识别、疾病辅助诊断、疾病风险预测、提供健康咨询服务等方面发挥越来越重要的作用。口腔黏膜病诊断和评估高度依赖形态学特征,AI在该领域具有广阔的应用前景。本文针对AI在口腔黏膜病辅助诊断、风险预测中的应用进行综述,并对当前面临的挑战和未来发展趋势进行分析和展望,以期为口腔黏膜病AI未来相关研究提供参考。

关键词: 人工智能, 深度学习, 口腔黏膜病, 辅助诊断, 风险预测

Abstract: With the development of artificial intelligence (AI) technology, its integrated application in the medical field has received widespread attention. Medical artificial intelligence plays an increasingly important role in image recognition, disease diagnosis, risk prediction, and provision of health consulting services. The diagnosis and evaluation of oral mucosal diseases are highly dependent on morphological characteristics, and AI has broad application prospects in this field. This article reviews the application of AI in auxiliary diagnosis and risk prediction of oral mucosal diseases, and analyzes and prospects the current challenges and future development trends, in order to provide reference for future AI-related research on oral mucosal diseases.

Key words: artificial intelligence, deep learning, oral mucosal diseases, auxiliary diagnosis, risk prediction

杨青默, 魏攀, 华红. 智能化时代下的口腔黏膜病学——人工智能应用研究的现状与展望[J]. 口腔医学研究, 2025, 41(1): 1-6.

YANG Qingmo, WEI Pan, HUA Hong. Oral Mucosal Diseases in the Era of Intelligence: Current Status and Prospects of Artificial Intelligence Applications in Research[J]. Journal of Oral Science Research, 2025, 41(1): 1-6.

参考文献

[1] Ge S, Liu L, Zhou Q, et al. Prevalence of and related risk factors in oral mucosa diseases among residents in the Baoshan District of Shanghai, China [J]. PeerJ, 2020, 8: e8644.
[2] Oivio UM, Pesonen P, Ylipalosaari M, et al. Prevalence of oral mucosal normal variations and lesions in a middle-aged population: a Northern Finland Birth Cohort 1966 study [J]. BMC Oral Health, 2020, 20(1): 357.
[3] Shulman JD. Prevalence of oral mucosal lesions in children and youths in the USA [J]. Int J Paediatr Dent, 2005, 15(2): 89-97.
[4] Cheruvathoor DD, Thomas V, Kumar NR, et al. High prevalence of oral mucosal lesions in elderly: Call for revolutionizing geriatric dental care strategies [J]. J Family Med Prim Care, 2020, 9(8): 4375-4380.
[5] Patil S, Doni B, Maheshwari S,et al. Prevalence and distribution of oral mucosal lesions in a geriatric Indian population [J]. Can Geriatr J, 2015, 18(1): 11-14.
[6] Khan A, Sohail A, Zahoora U, et al. A survey of the recent architectures of deep convolutional neural networks [J]. Artif Intell Rev, 2020, 53(8): 5455-5516.
[7] Krishnan MMR, Venkatraghavan V, Acharya UR, et al. Automated oral cancer identification using histopathological images: a hybrid feature extraction paradigm [J]. Micron, 2012, 43(2-3): 352-364.
[8] Kim JS, Kim BG, Hwang SH. Efficacy of artificial intelligence-assisted discrimination of oral cancerous lesions from normal mucosa based on the oral mucosal image: A systematic review and meta-analysis [J]. Cancers (Basel), 2022, 14(14):3499.
[9] Alom MZ, Taha TM, Yakopcic C, et al. The history began from alexnet: A comprehensive survey on deep learning approaches [J]. arXiv, 2018.
[10] Welikala RA, Remagnino P, Lim JH, et al. Automated detection and classification of oral lesions using deep learning for early detection of oral cancer [J]. IEEE Access, 2020, 8: 132677-132693.
[11] Tanriver G, Soluk Tekkesin M, Ergen O. Automated detection and classification of oral lesions using deep learning to detect oral potentially malignant disorders [J]. Cancers, 2021, 13(11): 2766.
[12] Sharma D, Kudva V, Patil V, et al. A convolutional neural network based deep learning algorithm for identification of oral precancerous and cancerous lesion and differentiation from normal mucosa: a retrospective study [J]. Eng Sci, 2022, 18: 278-287.
[13] Jubair F, Al-karadsheh O, Malamos D, et al. A novel lightweight deep convolutional neural network for early detection of oral cancer [J]. Oral Dis, 2022, 28(4): 1123-1130.
[14] Dubuc A, Zitouni A, Thomas C, et al. Improvement of mucosal lesion diagnosis with machine learning based on medical and semiological data: An observational study [J]. J Clin Med, 2022, 11(21): 6596.
[15] Idrees M, Farah CS, Shearston K, et al. A machine-learning algorithm for the reliable identification of oral lichen planus [J]. J Oral Pathol Med, 2021, 50(9): 946-953.
[16] Keser G, Bayrakdar iŞ, Pekiner FN, et al. A deep learning algorithm for classification of oral lichen planus lesions from photographic images: A retrospective study [J]. J Stomatol Oral Maxillofac Surg, 2023, 124(1): 101264.
[17] Xie F, Xu P, Xi X, et al. Oral mucosal disease recognition based on dynamic self-attention and feature discriminant loss [J]. Oral Dis, 2024, 30(5):3094-3107.
[18] Jurczyszyn K, Gedrange T, Kozakiewicz M. Theoretical background to automated diagnosing of oral leukoplakia: A preliminary report [J]. J Healthc Eng, 2020, 2020: 8831161.
[19] Anantharaman R, Anantharaman V, Lee Y. Oro vision: Deep learning for classifying orofacial diseases [C]. 2017 IEEE International Conference on Healthcare Informatics (ICHI): 39-45.
[20] Guo J, Wang H, Xue X, et al. Real-time classification on oral ulcer images with residual network and image enhancement [J]. IET Image Process, 2022, 16: 641-646.
[21] Gomes RFT, Schmith J, Figueiredo RM, et al. Use of artificial intelligence in the classification of elementary oral lesions from clinical images [J]. Int J Environ Res Public Health, 2023, 20(5): 3894.
[22] Liu Y, Li Y, Fu Y, et al. Quantitative prediction of oral cancer risk in patients with oral leukoplakia [J]. Oncotarget, 2017, 8(28): 46057-46064.
[23] Wang X, Yang J, Wei C, et al. A personalized computational model predicts cancer risk level of oral potentially malignant disorders and its web application for promotion of non-invasive screening [J]. J Oral Pathol Med, 2020, 49(5): 417-426.
[24] Adeoye J, Koohi-Moghadam M, Lo AWI, et al. Deep learning predicts the malignant-transformation-free survival of oral potentially malignant disorders [J]. Cancers (Basel), 2021, 13(23): 6054.
[25] Alhazmi A, Alhazmi Y, Makrami A, et al. Application of artificial intelligence and machine learning for prediction of oral cancer risk [J]. J Oral Pathol Med, 2021, 50(5): 444-450.
[26] Adeoye J, Koohi-Moghadam M, Choi SW, et al. Predicting oral cancer risk in patients with oral leukoplakia and oral lichenoid mucositis using machine learning [J]. J Big Data, 2023, 10(1): 1-24.
[27] Krishnan MMR, Pal M, Bomminayuni SK, et al. Automated classification of cells in sub-epithelial connective tissue of oral sub-mucous fibrosis-An SVM based approach [J]. Comput Biol Med, 2009, 39(12): 1096-1104.
[28] Omiye JA, Gui H, Daneshjou R, et al. Principles, applications, and future of artificial intelligence in dermatology [J]. Front Med (Lausanne), 2023, 10: 1278232.
[29] Bohr A, Memarzadeh K. The rise of artificial intelligence in healthcare applications [J]. Artif Intell Healthcare, 2020: 25-60.
[30] Hegde S, Ajila V, Zhu W, et al. Artificial intelligence in early diagnosis and prevention of oral cancer [J]. Asia Pac J Oncol Nurs, 2022, 9(12): 100133.
[31] Bajwa J, Munir U, Nori A, et al. Artificial intelligence in healthcare: transforming the practice of medicine [J]. Future Healthc J, 2021, 8(2): e188.
[32] Haron N, Zain RB, Ramanathan A, et al. m-Health for early detection of oral cancer in low-and middle-income countries [J]. Telemed e-Health, 2020, 26(3): 278-285.
[33] Alowais SA, Alghamdi SS, Alsuhebany N, et al. Revolutionizing healthcare: the role of artificial intelligence in clinical practice [J]. BMC Med Educ, 2023, 23(1): 689.
[34] Chollet F. Xception: Deep learning with depthwise separable convolutions [C]. In Proceedings of the IEEE conference on computer vision and pattern recognition, 2017: 1251-1258.
[35] Uthoff R, Song B, Sunny S, et al. Small form factor, flexible, dual-modality handheld probe for smartphone-based, point-of-care oral and oropharyngeal cancer screening [J]. J Biomed Opt, 2019, 24(10):1-8.

智能化时代下的口腔黏膜病学——人工智能应用研究的现状与展望

Oral Mucosal Diseases in the Era of Intelligence: Current Status and Prospects of Artificial Intelligence Applications in Research

PDF (PC)

可视化

摘要/Abstract

引用本文

使用本文

参考文献

相关文章 15

编辑推荐

Metrics

[1]	崔宇琛, 谢元栋, 吴聿淼, 牛凌霄, 常路广达, 朱宪春. 基于ViT-B深度学习模型的口腔良恶性病变图像分类研究[J]. 口腔医学研究, 2025, 41(1): 16-20.
[2]	王蓉, 帕克扎提·色依提, 王铁梅, 张懿, 钱堃. 比较Mask-RCNN与Mimics在上颌窦建模中的应用[J]. 口腔医学研究, 2024, 40(6): 550-554.
[3]	苏莉雯, 吴杨. 生成式人工智能在口腔医学的应用潜力与挑战[J]. 口腔医学研究, 2024, 40(1): 11-17.
[4]	古力孜巴·艾海提, 张鹏鑫, 齐佳, 亚尔肯·阿吉, 许辉. 腭裂术后腭咽闭合不全患者面部扭曲行为与腭咽闭合程度的关系[J]. 口腔医学研究, 2023, 39(8): 696-700.
[5]	马雪纯, 黄瀚霄, 申晓靖, 宁楠, 柳忠豪. 种植术区CBCT影像智能测量系统的初步研发与验证[J]. 口腔医学研究, 2023, 39(6): 491-495.
[6]	温佳欢, 傅裕杰, 任根强, 陈宇飞, 张旗. 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.
[7]	杨可, 孙雅楠, 胡雅英, 吕宜楠, 郑小风, 李易玮, 张佳莉. cyclin D1表达模式深度学习识别和预测模型对HPV阴性口腔鳞状细胞癌和口咽鳞状细胞癌患者预后的预测作用[J]. 口腔医学研究, 2023, 39(4): 308-315.
[8]	周刚, 何明靖. 口腔黏膜疑难疾病的诊断思维[J]. 口腔医学研究, 2023, 39(12): 1033-1036.
[9]	赖丹琳, 许亮, 倪涧钊, 朱小峰, 黄晓红. 不同层数的Mask-RCNN模型自动检测成釉细胞瘤效能的比较[J]. 口腔医学研究, 2023, 39(12): 1092-1096.
[10]	齐帅, 张旗. 卷积神经网络在牙体牙髓病影像诊断中的研究和应用[J]. 口腔医学研究, 2023, 39(11): 960-964.
[11]	刘鎵仪, 周彦恒. 人工智能在正畸诊疗及疗效预测中的应用进展[J]. 口腔医学研究, 2023, 39(10): 862-865.
[12]	吴天赐, 郁佳鑫, 黄晓峰, 陈盛, 王育新, 蒲玉梅. 深度学习图片分类模型ResNet-18用于判定口腔鳞状细胞癌浸润方式的初步研究[J]. 口腔医学研究, 2023, 39(10): 917-922.
[13]	雷尚雪, 但红霞. 光动力治疗增效研究新进展[J]. 口腔医学研究, 2022, 38(6): 497-500.
[14]	朱红标, 刘强冬, 曾子强, 娄伟明, 戴芳, 吴婧婷, 邓恬, 邓立彬, 宋莉. 基于口内数码照图像深度学习的牙周病早期筛查研究[J]. 口腔医学研究, 2022, 38(11): 1092-1095.
[15]	张畅, 刘洋. 光学相干断层扫描技术在口腔黏膜病中的应用[J]. 口腔医学研究, 2022, 38(10): 917-921.