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

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. Temporomandibular disorders (TMDs) affect the jaw joint and the surrounding muscles responsible for movement and chewing, often leading to chronic facial pain and impaired quality of life. However, it remains challenging to predict which individuals will develop chronic symptoms. In this study, we analyzed both clinical variables and magnetic resonance imaging (MRI) of the temporomandibular joint to identify factors associated with chronic TMD. MRI is a non-invasive imaging technique that provides high-resolution images of joint structures. We found that joint noise, bruxism, poor sleep quality, and structural damage on MRI were more frequently observed in patients with chronic TMD. Using these features, we developed artificial intelligence models to predict chronic cases, which may help guide early intervention and personalized care. Lee et al. analyse clinical, behavioural, and MRI features in patients with temporomandibular disorders to identify predictors of chronic pain. Using machine learning and deep neural networks, authors develop models that accurately predict chronicity based on jaw joint damage, bruxism, sleep disturbance, and symptom duration.