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

作者：Yeon‐Hee Lee, Seonggwang Jeon, Dohoon Kim, Q‐Schick Auh, Jeong Hoon Lee, Yung‐Kyun Noh · 发表于：Communications Medicine · 年份：2025 · DOI：10.1038/s43856-025-01081-5 · 被引用次数：4 · 研究领域：Temporomandibular Joint Disorders、Obstructive Sleep Apnea Research、Total Knee Arthroplasty Outcomes

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-Ba...