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中华口腔医学研究杂志(电子版) ›› 2026, Vol. 20 ›› Issue (01) : 25 -33. doi: 10.3877/cma.j.issn.1674-1366.2026.01.004

数智口腔专栏·综述

人工智能辅助参与牙体牙髓基础与临床研究
王祝愉, 权晶晶()   
  1. 中山大学附属口腔医院,光华口腔医学院,广东省口腔医学重点实验室,广东省口腔疾病临床医学研究中心,广州 510055
  • 收稿日期:2025-12-27 出版日期:2026-02-01
  • 通信作者: 权晶晶

Artificial intelligence-assisted basic and clinical research in endodontics

Zhuyu Wang, Jingjing Quan()   

  1. Hospital of Stomatology, Guanghua School of Stomatology, Sun Yat-sen University, Guangdong Provincial Key Laboratory of Stomatology, Guangdong Provincial Clinical Research Center of Oral Diseases, Guangzhou 510055, China
  • Received:2025-12-27 Published:2026-02-01
  • Corresponding author: Jingjing Quan
引用本文:

王祝愉, 权晶晶. 人工智能辅助参与牙体牙髓基础与临床研究[J/OL]. 中华口腔医学研究杂志(电子版), 2026, 20(01): 25-33.

Zhuyu Wang, Jingjing Quan. Artificial intelligence-assisted basic and clinical research in endodontics[J/OL]. Chinese Journal of Stomatological Research(Electronic Edition), 2026, 20(01): 25-33.

人工智能(AI)技术正在深刻改变牙体牙髓病学的基础与临床研究范式。在基础研究中,AI通过高精度影像分割、多模态数据整合与生物信息学分析,助力科研人员深入探索牙髓牙本质复合体的结构特征、生物学机制及疾病发生、发展规律。在临床研究中,AI优化了研究设计、疾病诊断与治疗结局预测全过程,提升了研究的科学性、效率与可重复性。尽管AI在数据质量、算法可解释性和跨学科融合等方面仍面临挑战,但其作为"科研协作者"的潜力日益凸显,有望推动牙体牙髓病学迈向更精准、高效与创新的研究新阶段。

Artificial intelligence (AI) is profoundly transforming the paradigms of basic and clinical research in endodontics. In basic research, AI facilitates the in-depth exploration of the structural characteristics, biological mechanisms, and disease pathogenesis of the pulp-dentin complex through high-precision image segmentation, multimodal data integration, and bioinformatics analysis. In clinical research, AI optimizes the entire process of study design, disease diagnosis, and treatment outcome prediction, enhancing the scientific rigor, efficiency, and reproducibility of research. Despite challenges related to data quality, algorithm interpretability, and interdisciplinary integration, AI's potential as a "research collaborator" is increasingly evident, promising to advance endodontics into a new era of more precise, efficient, and innovative research.

表1 人工智能(AI)在实验智能设计与材料性能预测中的应用
表2 人工智能(AI)在生物材料/组织工程研究中的应用
研究方向 AI技术 数据来源 研究成果 科研意义
抗菌性根管充填材料开发 机器学习 实验合成与表征等 开发出Ag-isoG超分子水凝胶,具有显著抗菌活性,细胞毒性低[24] 提供一种快速、稳定、抗菌性强且生物相容性好的根管消毒与充填新材料
水凝胶生物材料性能预测 梯度提升、随机森林等 组合化学库、凝胶实验数据等 预测水凝胶形成准确率达62%;识别出Fmoc-氨基酸等关键结构特征与凝胶能力相关[25] 建立"化学结构-凝胶性能"预测模型,加速新型生物材料智能筛选与设计
生物活性分子虚拟筛选 网络药理学、分子对接等 化合物数据库、蛋白质结构数据库、生物活性数据集等 预测分子与靶点相互作用,筛选具有牙髓保护或再生诱导潜力的生物活性分子[27] 提高药物发现效率,为牙髓再生提供新型诱导分子或组合疗法
干细胞与材料相互作用分析 卷积神经网络等 干细胞图像数据、基因表达谱、细胞形态与分化标志物等 自动识别干细胞分化状态、衰老标志,预测细胞与材料相互作用效果[28,29] 实现细胞-材料界面的智能化、高通量分析,指导材料表面改性与生物功能调控
组织工程支架设计与优化 机器学习、生成对抗网络等 显微CT、机械性能数据、生物相容性数据库等 生成具有理想机械强度与生物相容性的支架结构,优化细胞附着于组织再生微环境[30] 实现个性化、功能化支架的智能设计,促进牙髓再生治疗
表3 人工智能(AI)在临床结局预测中的典型应用
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