Faculty of Nursing, Chulalongkorn University, Bangkok, Bangkok, 10330, Thailand.
Department of Oral Biology, Dental Pharmacology, Faculty of Dental Medicine, Universitas Airlangga, Surabaya, East Java, 60132, Indonesia.
F1000Res. 2024 Oct 11;13:908. doi: 10.12688/f1000research.154704.3. eCollection 2024.
Dental caries is a common chronic oral disease, posing a serious public health issue. By analyzing large datasets, machine learning shows potential in addressing this problem. This study employs bibliometric analysis to explore emerging topics, collaborations, key authors, and research trends in Southeast Asia related to the application of machine learning in dental caries management.
A comprehensive selection using the Scopus database to obtain relevant research, covering publications from inception to July 2024 was done. We employed the Bibliometric approaches, including co-authorship networks, yearly publishing trends, institutional and national partnerships, keyword co-occurrence analysis, and citation analysis, for the collected data. To explore the visualization and network analysis, we employed the tools such as VOSviewer and Bibliometrix in R package.
The final bibliometric analysis included 246 papers. We found that Malaysia became the top contributor with 59 publications, followed by Indonesia (37) and Thailand (29). Malaysia had the highest Multiple Country Publications (MCP) ratio at 0.407. Top institutions including the Universiti Sains Malaysia led with 39 articles, followed by Chiang Mai University (36) and the National University of Singapore (30) became the leader. Co-authorship analysis using VOSviewer revealed six distinct clusters. A total of 1220 scholars contributed to these publications. The top 10 keywords, including 'human' and 'dental caries,' indicated research hotspots.
We found growing evidence of machine learning applications to address dental caries in Southeast Asia. The bibliometric analysis highlights key authors, collaborative networks, and emerging topics, revealing research trends since 2014. This study underscores the importance of bibliometric analysis in tackling this public health issue.
龋齿是一种常见的慢性口腔疾病，对公共健康构成严重威胁。通过分析大型数据集，机器学习在解决这一问题方面显示出了潜力。本研究采用文献计量学分析方法，探讨了东南亚地区机器学习在龋齿管理应用方面的新兴主题、合作关系、主要作者和研究趋势。
我们通过 Scopus 数据库进行全面检索，获取了 2014 年至 2024 年 7 月期间的相关研究。我们采用了文献计量学方法，包括合著网络、年度出版趋势、机构和国家合作关系、关键词共现分析和引文分析，对收集到的数据进行了分析。为了探索可视化和网络分析，我们使用了 VOSviewer 和 R 包中的 Bibliometrix 等工具。
最终的文献计量学分析共纳入了 246 篇论文。我们发现，马来西亚以 59 篇论文成为发文量最多的国家，其次是印度尼西亚（37 篇）和泰国（29 篇）。马来西亚的多国合作论文比例最高，为 0.407。发文量排名前 10 的机构包括马来西亚理科大学（39 篇）、清迈大学（36 篇）和新加坡国立大学（30 篇）。VOSviewer 中的合著网络分析揭示了六个不同的聚类。共有 1220 位学者为这些论文做出了贡献。排名前 10 的关键词包括“人”和“龋齿”，表明了研究热点。
我们发现越来越多的证据表明机器学习在东南亚地区被用于解决龋齿问题。文献计量学分析突出了主要作者、合作网络和新兴主题，揭示了自 2014 年以来的研究趋势。本研究强调了文献计量学分析在解决这一公共卫生问题中的重要性。
