Bibliometric analysis of deep learning applications in dentistry

Abstract

Deep learning techniques have significantly impacted various aspects of dentistry, including diagnosis, treatment planning, and patient care. This paper aims to explore the influence and patterns of deep learning methodologies in dentistry using bibliometric analysis. A comprehensive search of the Web of Science database was conducted, yielding 1,228 relevant articles published between 2014 and 2024. Bibliometric analyses, including keyword co-occurrence, author co-authorship, country co-authorship, author citation, document bibliographic coupling, organization bibliographic coupling, and reference co-citation, were performed using VOSviewer software. Among the total publications, a remarkable 94.95% were published after 2018. The United States and Japan emerged as the leading nations, contributing 20.6% and 18.48% of the articles, respectively. Tokyo Medical and Dental University stood out among 2,090 organizations with the highest publication count of 88. Notable authors such as Orhan, Kaan, and Schwendicke, Falk, each contributed 28 publications, closely followed by Ariji, Yoshiko, with 27 publications, and Ariji, Eiichiro, with 26 publications. The analysis revealed clusters of interconnected research areas, collaborative networks among authors and countries, and influential publications. This study provides valuable insights into the evolving landscape of deep learning in dentistry, guiding future research directions and promoting interdisciplinary collaboration.

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