Akademik Veri Yönetim Sistemi
HRU International Journal of Dentistry and Oral Research, cilt.5, sa.3, ss.159-173, 2025 (Hakemli Dergi)
Abstract
Background: This study aims to systematically map the thematic evolution of temporomandibular joint (TMJ)
research from 2000 to 2025, identifying shifts in clinical and methodological priorities using transformer-based topic
modelling.
Materials and Method: A total of 18,107 TMJ-related publications were retrieved from the Scopus database. Titles
and abstracts were embedded using a biomedical transformer model. Dimensionality reduction was performed via
UMAP, followed by clustering with HDBSCAN. Topics were labelled using class-based TF–IDF. Annual topic
proportions were analyzed using ordinary least squares regression with false discovery rate correction.
Results: Twenty-seven distinct topics were identified across six domains: biomechanics, clinical symptoms, imaging,
pathology, surgery, and therapies. Eleven topics showed significant upward trends, notably arthrocentesis, ankylosis,
and mouth-opening interventions. Six topics declined, including disc-focused MRI metrics and elastography. Ten
topics remained stable. The findings reveal a thematic shift from biomechanical and imaging metrics toward
minimally invasive interventions and digital health applications.
Conclusions: Transformer-based topic modelling offers a panoramic view of TMJ research trends, highlighting a
growing focus on patient-centred, conservative, and technology-assisted care. These insights provide a framework for
future research prioritization and underscore the need for interdisciplinary approaches.
Research Article (HRU Int J Dent Oral Res 2025; 5(3):159-173)
Keywords: Artificial intelligence, natural language processing, temporomandibular disorders, temporomandibular
joints.