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Clinical and MRI markers for acute vs chronic temporomandibular disorders using a machine learning and deep neural networks.

Authors

Lee YH,Jeon S,Kim DH,Auh QS,Lee JH,Noh YK

Affiliations (7)

  • Department of Orofacial Pain and Oral Medicine, Kyung Hee University Dental Hospital, Kyung Hee University School of Dentistry, Seoul, Korea. [email protected].
  • Center for Systems Biology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA. [email protected].
  • Department of Computer Science, Hanyang University, Seoul, Korea.
  • Department of Orofacial Pain and Oral Medicine, Kyung Hee University Dental Hospital, Kyung Hee University School of Dentistry, Seoul, Korea.
  • Department of Radiology, Stanford University School of Medicine, Stanford, CA, USA.
  • Department of Computer Science, Hanyang University, Seoul, Korea. [email protected].
  • Affiliate Professor, School of Computational Sciences, Korea Institute for Advanced Study (KIAS), Seoul, Korea. [email protected].

Abstract

Exploring the transition from acute to chronic temporomandibular disorders (TMD) remains challenging due to the multifactorial nature of the disease. This study aims to identify clinical, behavioral, and imaging-based predictors that contribute to symptom chronicity in patients with TMD. We enrolled 239 patients with TMD (161 women, 78 men; mean age 35.60 ± 17.93 years), classified as acute ( < 6 months) or chronic ( ≥ 6 months) based on symptom duration. TMD was diagnosed according to the Diagnostic Criteria for TMD (DC/TMD Axis I). Clinical data, sleep-related variables, and temporomandibular joint magnetic resonance imaging (MRI) were collected. MRI assessments included anterior disc displacement (ADD), joint space narrowing, osteoarthritis, and effusion using 3 T T2-weighted and proton density scans. Predictors were evaluated using logistic regression and deep neural networks (DNN), and performance was compared. Chronic TMD is observed in 51.05% of patients. Compared to acute cases, chronic TMD is more frequently associated with TMJ noise (70.5%), bruxism (31.1%), and higher pain intensity (VAS: 4.82 ± 2.47). They also have shorter sleep and higher STOP-Bang scores, indicating greater risk of obstructive sleep apnea. MRI findings reveal increased prevalence of ADD (86.9%), TMJ-OA (82.0%), and joint space narrowing (88.5%) in chronic TMD. Logistic regression achieves an AUROC of 0.7550 (95% CI: 0.6550-0.8550), identifying TMJ noise, bruxism, VAS, sleep disturbance, STOP-Bang≥5, ADD, and joint space narrowing as significant predictors. The DNN model improves accuracy to 79.49% compared to 75.50%, though the difference is not statistically significant (p = 0.3067). Behavioral and TMJ-related structural factors are key predictors of chronic TMD and may aid early identification. Timely recognition may support personalized strategies and improve outcomes.

Topics

Journal Article

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