ArticleCommunications medicine2025
Clinical and MRI markers for acute vs chronic temporomandibular disorders using a machine learning and deep neural networks.
Article in Communications medicine, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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The trial behind it
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Who cites it
4 citing papers in PubMed.
- Artificial Intelligence-Based Approach for Determining the Risk of Temporomandibular Disorders.Journal of oral rehabilitation · 2026Article
- Artificial intelligence-based prognostic modeling in temporomandibular disorders and chronic orofacial pain: a critical conceptual review.Journal of oral & facial pain and headache · 2026Review
- Immuno-Mechanical Signaling Network Integration in Temporomandibular Joint Pathology: A TMID Conceptual Framework.International journal of molecular sciences · 2026Review
- A cross-sectional study investigating the prevalence and risk factors of temporomandibular disorders (TMDs) in Chinese medical and dental students.BMC medical education · 2026Article
Corrections and comments
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Authors and funding
6 authors.
Funding
Abstract
backgroundExploring 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.
methodsWe 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.
resultsChronic 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).
conclusionsBehavioral 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.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.