Trends of machine learning for dental caries research in Southeast Asia: insights from a bibliometric analysis [version 1; peer review: awaiting peer review]

• Published in: F1000 research Vol. 13; p. 908
• Main Authors: Hasan, Faizul, Setia Budi, Hendrik, Yuliana, Lia Taurussia, Sujarwadi, Mokh
• Format: Journal Article
• Language: English
• Published: England F1000 Research Ltd 2024
• Subjects: Asia, Southeastern - epidemiology; Bibliometrics; Dental Caries - epidemiology; dental caries; transmissible disease; health and well-being; machine learning; artificial intelligence; eng; Humans; Machine Learning; Asia, Southeastern; dental caries; transmissible disease; health and well-being; machine learning; artificial intelligence
• ISSN: 2046-1402; 2046-1402
• DOI: 10.12688/f1000research.154704.1

Background 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. Methods 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. Results 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. Conclusion 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.