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

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

Bibliometrics; Temporomandibular disorders; Magnetic resonance imaging; Artificial intelligence; Research trends

[1] Lee YH, Won JH, Kim S, Auh QS, Noh YK. Advantages of deep learning with convolutional neural network in detecting disc displacement of the temporomandibular joint in magnetic resonance imaging. Scientific Reports. 2022; 12: 11352.

[2] Valesan LF, Da-Cas CD, Réus JC, Denardin ACS, Garanhani RR, Bonotto D, et al. Prevalence of temporomandibular joint disorders: a systematic review and meta-analysis. Clinical Oral Investigations. 2021; 25: 441–453.

[3] da Silva ZA, Melo WWP, Ferreira HHN, Lima RR, Souza-Rodrigues RD. Global trends and future research directions for temporomandibular disorders and stem cells. Journal of Functional Biomaterials. 2023; 14: 103.

[4] Ferrillo M, Giudice A, Marotta N, Fortunato F, Di Venere D, Ammendolia A, et al. Pain management and rehabilitation for central sensitization in temporomandibular disorders: a comprehensive review. International Journal of Molecular Sciences. 2022; 23: 12164.

[5] List T, Jensen RH. Temporomandibular disorders: old ideas and new concepts. Cephalalgia. 2017; 37: 692–704.

[6] Barbosa C, Gavinha S, Soares T, Reis T, Manso C. Is generalized joint hypermobility associated with chronic painful temporomandibular disorders in young adults? A cross-sectional study. Journal of Clinical Medicine. 2024; 14: 44.

[7] Johnson M, Sreela LS, Mathew P, Prasad TS. Actual applications of magnetic resonance imaging in dentomaxillofacial region. Oral Radiology. 2022; 38: 17–28.

[8] Stegenga B. Nomenclature and classification of temporomandibular joint disorders. Journal of Oral Rehabilitation. 2010; 37: 760–765.

[9] Larheim TA. Role of magnetic resonance imaging in the clinical diagnosis of the temporomandibular joint. Cells Tissues Organs. 2005; 180: 6–21.

[10] Chen C. CiteSpace II: detecting and visualizing emerging trends and transient patterns in scientific literature. Journal of the American Society for Information Science and Technology. 2006; 57: 359–377.

[11] Chen C. Searching for intellectual turning points: progressive knowledge domain visualization. Proceedings of the National Academy of Sciences. 2004; 101: 5303–5310.

[12] Schiffman E, Ohrbach R, Truelove E, Look J, Anderson G, Goulet JP, et al.; International RDC/TMD Consortium Network, International association for Dental Research; Orofacial Pain Special Interest Group, International Association for the Study of Pain. Diagnostic criteria for temporomandibular disorders (DC/TMD) for clinical and research applications: recommendations of the International RDC/TMD Consortium Network* and Orofacial Pain Special Interest Group†. Journal of Oral & Facial Pain and Headache. 2014; 28: 6–27.

[13] Tasaki MM, Westesson P, Isberg AM, Ren Y, Tallents RH. Classification and prevalence of temporomandibular joint disk displacement in patients and symptom-free volunteers. American Journal of Orthodontics and Dentofacial Orthopedics. 1996; 109: 249–262.

[14] Katzberg RW, Westesson P, Tallents RH, Drake CM. Anatomic disorders of the temporomandibular joint disc in asymptomatic subjects. Journal of Oral and Maxillofacial Surgery. 1996; 54: 147–153.

[15] Ahmad M, Hollender L, Anderson Q, Kartha K, Ohrbach R, Truelove EL, et al. Research diagnostic criteria for temporomandibular disorders (RDC/TMD): development of image analysis criteria and examiner reliability for image analysis. Oral Surgery, Oral Medicine, Oral Pathology, Oral Radiology, and Endodontology. 2009; 107: 844–860.

[16] Matsubara R, Yanagi Y, Oki K, Hisatomi M, Santos KC, Bamgbose BO, et al. Assessment of MRI findings and clinical symptoms in patients with temporomandibular joint disorders. Dento Maxillo Facial Radiology. 2018; 47: 20170412.

[17] Vogl TJ, Lauer H, Lehnert T, Naguib NNN, Ottl P, Filmann N, et al. The value of MRI in patients with temporomandibular joint dysfunction: correlation of MRI and clinical findings. European Journal of Radiology. 2016; 85: 714–719.

[18] Chen C, Dubin R, Kim MC. Emerging trends and new developments in regenerative medicine: a scientometric update (2000–2014). Expert Opinion on Biological Therapy. 2014; 14: 1295–1317.

[19] Zhao R, Xiong X, Li Z, Zhang L, Yang H, Ye Z. Recent advances and educational strategies in diagnostic imaging for temporomandibular disorders: a narrative literature review. Frontiers in Neurology. 2025; 16: 1597312.

[20] Öcbe M. The evolving role of MRI in dentomaxillofacial diagnostics: a comprehensive review. European Oral Research. 2025; 59: 58–67.

[21] Imoto K, Otonari-Yamamoto M, Nishikawa K, Sano T, Yamamoto A. Potential of fluid-attenuated inversion recovery (FLAIR) in identification of temporomandibular joint effusion compared with T2-weighted images. Oral Surgery, Oral Medicine, Oral Pathology, Oral Radiology, and Endodontology. 2011; 112: 243–248.

[22] Yura S, Nobata K, Shima T. Diagnostic accuracy on fat-saturated T2 weighted MRI for diagnosis of intra-articular adhesions of the temporomandibular joint. Dento Maxillo Facial Radiology. 2012; 41: 230–233.

[23] Al-Haj Husain A, Schmidt V, Valdec S, Stadlinger B, Winklhofer S, Schönegg D, et al. MR-orthopantomography in operative dentistry and oral and maxillofacial surgery: a proof of concept study. Scientific Reports. 2023; 13: 6228.

[24] Sawada E, Kaneda T, Sakai O, Kawashima Y, Ito K, Hirahara N, et al. Increased apparent diffusion coefficient values of masticatory muscles on diffusion-weighted magnetic resonance imaging in patients with temporomandibular joint disorder and unilateral pain. Journal of Oral and Maxillofacial Surgery. 2019; 77: 2223–2229.

[25] Lin X, Guo W, She D, Hu J, Dai H, Song Y, et al. Three-dimensional architecture characteristics and diffusion properties of masticatory muscles assessed with diffusion tensor imaging and diffusion spectrum imaging: a pilot study of differences, reproducibility and sensitivity to microenvironment changes. BMC Musculoskeletal Disorders. 2025; 26: 407.

[26] Vaddi A, Parasher P, Khurana S. Beyond X-rays: unveiling the future of dental diagnosis with dental magnetic resonance imaging. Diagnostics. 2025; 15: 1153.

[27] von Kalle T, Winkler P, Stuber T. Contrast-enhanced MRI of normal temporomandibular joints in children—is there enhancement or not? Rheumatology. 2013; 52: 363–367.

[28] Jeon KJ, Choi YJ, Lee C, Kim HS, Han SS. Evaluation of masticatory muscles in temporomandibular joint disorder patients using quantitative MRI fat fraction analysis-could it be a biomarker? PLOS ONE. 2024; 19: e0296769.

[29] Li X, Jiang Y, Chen Z. Altered grey matter volume and cerebral perfusion over the whole brain in painful temporomandibular disorders: a pilot voxel-based analysis. Current Medical Imaging. 2025; 21: e15734056373583.

[30] Collin M, Hagelberg S, Ernberg M, Hedenberg‐Magnusson B, Christidis N. Temporomandibular joint involvement in children with juvenile idiopathic arthritis—symptoms, clinical signs and radiographic findings. Journal of Oral Rehabilitation. 2022; 49: 37–46.

[31] Yu Y, Wu SJ, Zhu YM. Deep learning-based automated diagnosis of temporomandibular joint anterior disc displacement and its clinical application. Frontiers in Physiology. 2024; 15: 1445258.

[32] Chan NHY, Ip CK, Li DTS, Leung YY. Diagnosis and treatment of myogenous temporomandibular disorders: a clinical update. Diagnostics. 2022; 12: 2914.

[33] Murali S, Ding H, Adedeji F, Qin C, Obungoloch J, Asllani I, et al. Bringing MRI to low- and middle-income countries: directions, challenges and potential solutions. NMR in Biomedicine. 2024; 37: e4992.

[34] Zhang M, Sang J, Yang C, Liu H, Xiao Y, Li J, et al. Cone-beam computed tomography and magnetic resonance imaging in temporomandibular joint disorder diagnosis: a comparative study. Journal of Multidisciplinary Healthcare. 2025; 18: 3793–3802.

[35] Wang K, Li C, Zhou J, Ren J, You M. Diagnostic accuracy of dynamic high-resolution ultrasonography in assessing anterior disc displacement in temporomandibular joint disorders: a prospective observational study. Healthcare. 2024; 12: 2355.

[36] Thapar PR, Nadgere JB, Iyer J, Salvi NA. Diagnostic accuracy of ultrasonography compared with magnetic resonance imaging in diagnosing disc displacement of the temporomandibular joint: a systematic review and meta-analysis. The Journal of Prosthetic Dentistry. 2025; 133: 446–454.

[37] Mehta V, Tripathy S, Noor T, Mathur A. Artificial intelligence in temporomandibular joint disorders: an umbrella review. Clinical and Experimental Dental Research. 2025; 11: e70115.

[38] Sankar H, Alagarsamy R, Lal B, Rana SS, Roychoudhury A, Barathi A, et al. Role of artificial intelligence in magnetic resonance imaging-based detection of temporomandibular joint disorder: a systematic review. British Journal of Oral and Maxillofacial Surgery. 2025; 63: 174–181.

[39] Manek M, Maita I, Bezerra Silva DF, Pita de Melo D, Major PW, Jaremko JL, et al. Temporomandibular joint assessment in MRI images using artificial intelligence tools: where are we now? A systematic review. Dento Maxillo Facial Radiology. 2025; 54: 1–11.

[40] Li DTS, Leung YY. Temporomandibular disorders: current concepts and controversies in diagnosis and management. Diagnostics. 2021; 11: 459.

[41] van Eck NJ, Waltman L. Software survey: VOSviewer, a computer program for bibliometric mapping. Scientometrics. 2010; 84: 523–538.