1. Topic Analysis of the Literature Reveals the Research Structure: A Case Study in Periodontics
- Author
-
Carlo Galli, Maria Teresa Colangelo, Marco Meleti, Stefano Guizzardi, and Elena Calciolari
- Subjects
periodontics ,trending topics ,natural language processing ,deep learning ,artificial intelligence ,Technology - Abstract
Periodontics is a complex field characterized by a constantly growing body of research, which poses a challenge for researchers and stakeholders striving to stay abreast of the evolving literature. Traditional bibliometric surveys, while accurate, are labor-intensive and not scalable to meet the demands of such rapidly expanding domains. In this study, we employed BERTopic, a transformer-based topic modeling framework, to map the thematic landscape of periodontics research published in MEDLINE from 2009 to 2024. We identified 31 broad topics encompassing four major thematic axes—patient management, periomedicine, oral microbiology, and implant-related surgery—thereby illuminating core areas and their semantic relationships. Compared with a conventional Latent Dirichlet Allocation (LDA) approach, BERTopic yielded more contextually nuanced clusters and facilitated the isolation of distinct, smaller research niches. Although some documents remained unlabeled, potentially reflecting either semantic ambiguity or niche topics below the clustering threshold, our results underscore the flexibility, interpretability, and scalability of neural topic modeling in this domain. Future refinements—such as domain-specific embedding models and optimized granularity levels—could further enhance the precision and utility of this method, ultimately guiding researchers, educators, and policymakers in navigating the evolving landscape of periodontics.
- Published
- 2025
- Full Text
- View/download PDF