Development of an Integrated Evaluation-to-Treatment Framework to Enhance Clinical Efficiency and Effectiveness in Temporomandibular Disorders
Development of an Integrated Evaluation-to-Treatment Framework to Enhance Clinical Efficiency and Effectiveness in Temporomandibular Disorders
This study aims to improve clinical efficiency and effectiveness in treating Temporomandibular Disorders (TMD). The researchers will integrate machine learning technology with clinical examination data, CBCT, and MRI to identify the core underlying issues of each patient. Based on this precise, AI-assisted diagnosis, the study will implement a tailored treatment framework. The final goal is to develop predictive algorithms that can determine MRI necessity while ensuring that clinical interventions are highly targeted, efficient, and successful.
This study integrates machine learning with a clinical trial framework to optimize the evaluation and management of Temporomandibular Disorders (TMD). A total of 180 eligible participants are expected to be enrolled in the study. To ensure a homogenous sample, strict screening will be conducted using standardized psychometric scales; patients with significant psychosocial confounders (defined as GAD-7 ≥10, PHQ-9 ≥15, PHQ-15 ≥10, or PSQI >5) or comorbidities such as fibromyalgia, rheumatic autoimmune diseases, and trauma history will be excluded. Prior to treatment, baseline clinical examination, cone beam computed tomography (CBCT), and magnetic resonance imaging (MRI) data will be collected. All clinical data and longitudinal pre- and post-treatment MRI findings will be utilized to train advanced multi-modal diagnostic models; based on this foundational intelligence, predictive algorithms will then be developed to determine the necessity of an MRI scan using only clinical data and CBCT images. The enrolled participants will be divided by active maximum mouth opening into two subgroups (≤30 mm and >30 mm) and randomly assigned to three treatment arms: (1) Proprioceptive Training + Sham + LLLT (2 weeks of sham laser followed by 2 weeks of active laser acupuncture); (2) Proprioceptive Training + LLLT + LLLT (4 weeks of active laser acupuncture); and (3) Waiting List Control (receiving standard conservative care only).Proprioceptive training includes gentle teeth tapping, tongue-to-palate contact, and neutral head-neck posture exercises, performed three times daily. LLLT targets the Xiaguan (ST7), Yifeng (SJ17), and bilateral Fengchi (GB20) points once weekly. Assessments will be conducted biweekly during treatment, with final evaluation at 4 weeks and follow-ups at 1, 3, 6, 12, and 24 months post-treatment. Post-treatment MRI will be performed at the 4-week endpoint to evaluate objective intra-articular and perimuscular soft-tissue changes, correlating with improvements in pain resolution efficiency and mandibular range of motion recovery.
Inclusion Criteria:
Exclusion Criteria:
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