2001—2023年机器学习在牙周疾病研究文献的可视化分析

doi:10.19439/j.sjos.2025.06.019

上海口腔医学 ›› 2025, Vol. 34 ›› Issue (6): 660-667.doi: 10.19439/j.sjos.2025.06.019

2001—2023年机器学习在牙周疾病研究文献的可视化分析

林惠立¹, 陈珺^1,2, 李文杰^1,3

1.中南大学湘雅口腔医学院, 口腔数智化与个体化诊疗技术湖南省工程研究中心, 口腔健康研究湖南省重点实验室, 湖南省口腔重大疾病与口腔健康临床医学研究中心, 口腔颌面再生王松灵院士专家工作站, 湖南长沙 410008;
2.中南大学湘雅口腔医院牙周病科, 3.正畸科, 湖南长沙 410008

收稿日期:2024-05-17 修回日期:2024-08-12 发布日期:2025-12-30
通讯作者: 陈珺,E-mail: chenjun1222@csu.edu.cn
作者简介:林惠立（2000-）,女,本科,E-mail: 2125413957 @qq.com
基金资助:
湖南省自然科学基金面上项目（2024JJ5506）; 中南大学研究生教育教学改革项目（2023JGB051, 2024JGB039）

A visual analysis of machine learning in periodontal disease research, 2001-2023

Lin Huili¹, Chen Jun^1,2, Li Wenjie^1,3

1. Hunan Engineering Research Center for Oral Digital Intelligence and Personalized Medicine; Hunan Key Laboratory of Oral Health Research; Hunan Clinical Research Center of Oral Major Diseases and Oral Health; Academician Workstation for Oral-maxillofacial and Regenerative Medicine and Xiangya School of Stomatology, Central South University. Changsha 410008;
2. Department of Periodontology, 3. Department of Orthodontics, Xiangya Stomatological Hospital, Central South University. Changsha 410008, Hunan Province, China

Received:2024-05-17 Revised:2024-08-12 Published:2025-12-30

摘要/Abstract

摘要： 目的:分析机器学习在牙周疾病的相关文献,了解其应用和研究热点。方法:运用Bibliometrix4.4.1、CiteSpace6.3.R1、VOSviewer1.16.18对Web of Science核心合集2001年1月1日至2023年12月31日收录的机器学习在牙周疾病研究的相关文献进行可视化分析。结果:共纳入127篇符合纳入和排除标准的文献,2018年之后每年的发文量及被引频次均呈快速上升趋势。相关文献高频关键词提示牙周炎、机器学习、深度学习等。近2年机器学习在牙周临床研究热点为基于影像进行分割和特征提取,机器学习在牙周临床研究集中在结合影像辅助诊断牙周病和种植体周围炎;机器学习在牙周的基础研究则集中在筛选牙周疾病与系统性疾病间的生物标志物。结论:文献可视化分析展现了机器学习在牙周疾病研究的应用与热点,可为今后的研究方向提供参考。

关键词: 机器学习, 牙周疾病, 文献计量学, 可视化分析

Abstract: PURPOSE: To analyze the literatures related to machine learning in periodontal diseases to understand its applications and research hotspots. METHODS: Literatures related to machine learning in periodontal disease researches included in the Web of Science Core Collection from January 1, 2001 to December 31, 2023 were visualized and analyzed using Bibliometrix 4.4.1, CiteSpace 6.3.R1, and VOSviewer 1.16.18. RESULTS: A total of 127 papers that met the inclusion and exclusion criteria were included, and the number of publications and citation frequency of each year after 2018 showed a rapid upward trend. The high-frequency keywords of related literatures suggested: periodontitis, machine learning, deep learning, and so on. The hotspot of machine learning in periodontal clinical research in the past 2 years was segmentation and feature extraction based on images. The clinical research of machine learning in periodontics focused on combining images to assist in the diagnosis of periodontal disease and peri-implantitis; the basic research of machine learning in periodontics focused on screening biomarkers between periodontal diseases and systemic diseases. CONCLUSIONS: The literature visualization analysis shows the application and hotspots of machine learning in periodontal disease research, which can provide a reference for future research direction.

Key words: Machine learning, Periodontal disease, Bibliometrics, Visual analysis

中图分类号:

R781.4

林惠立, 陈珺, 李文杰. 2001—2023年机器学习在牙周疾病研究文献的可视化分析[J]. 上海口腔医学, 2025, 34(6): 660-667.

Lin Huili, Chen Jun, Li Wenjie. A visual analysis of machine learning in periodontal disease research, 2001-2023[J]. Shanghai Journal of Stomatology, 2025, 34(6): 660-667.

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2001—2023年机器学习在牙周疾病研究文献的可视化分析

A visual analysis of machine learning in periodontal disease research, 2001-2023

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