Global research trends in MRI of temporomandibular disorders: a bibliometric study and visualization analysis via...

To conduct a bibliometric analysis mapping the intellectual landscape and emerging trends in Magnetic resonance imaging (MRI) research of temporomandibular disorders (TMD), identifying knowledge gaps and future directions.

A total of 1017 articles were retrieved from the Web of Science Core Collection (WOSCC) database from 1995 to 2024 using the search formula: TS (Topic Search) = ("Temporomandibular Disorders" OR "Temporomandibular Joint Disease" OR TMD) AND TS = ("Magnetic Resonance Imaging" OR MRI). Author/country collaboration network, co-citation analysis, keyword clustering, and burst detection were conducted via CiteSpace 6.4.R1 (Parameters: Time slice: 1 year; g-index k = 25; Log-Likelihood Ratio clustering).

Annual publications exhibited triphasic growth, peaking at 75 articles in 2022. The United States (160 articles, centrality = 0.22) dominated global collaborations, while Shanghai Jiao Tong University emerged as the most productive institution (30 articles). Key clusters revealed 15 clusters (Q = 0.4235, S = 0.7521), with core clusters including "juvenile idiopathic arthritis" and "deep learning". Burst strength identified four major research frontier directions: analysis of pathological characteristics of diseases (morphology with a burst strength of 4.49; anterior disc displacement with a burst strength of 3.69); innovation in imaging examination methods (ultrasonography with a burst strength of 3.61; cone-beam computed tomography with a burst strength of 3.51); development of intelligent diagnostic technologies (diagnostic criteria with a burst strength of 12.09; deep learning with a burst strength of 5.81); and support for clinical management applications (management with a burst strength of 4.62).

This study delineates an evolving research paradigm integrating Artificial Intelligence (AI)-driven diagnostics with multimodal imaging.