Comprehensive Patient-Specific Prediction Models for Diagnosis and Prognosis of Temporoman-dibular Joint Osteoarthritis

Abstract

Osteoarthritis is the most common degenerative joint disease, affecting 15% of the global popula-tion. Osteoarthritis in temporomandibular joint (TMJ OA) can cause chronic pain, facial deformi-ty, joint dysfunction, impacting the quality of life. Unlike weight-bearing joints, TMJ OA primar-ily affects individuals between the ages of 20 and 40 and can also appear in adolescents. Current standards for diagnosing TMJ OA rely on clinical and imaging criteria. However, these criteria have limited efficacy in detecting early-stage TMJ OA, posing challenges to timely inter-vention and mitigation of irreversible tissue damage. Hence, it becomes imperative to identify additional objective diagnostic criteria. In addition, determining which patients are at increased risk of disease progression is critical for making informed clinical decisions and designing more effective and individualized treatments. Radiomics is a newly established field propelled by advancements in computational power. It extracts quantitative imaging features from radiological images, aiming to identify subtle tissue variations and reduce subjectivity in image interpretation. Beyond radiomics, metabolic abnor-malities in joint tissues serve as early indicators of osteoarthritis. Although there has been pro-gress in studying osteoarthritis biomarkers, they have not yet been clinically established. Evaluat-ing multiple markers may reveal their intricate interrelations and fully harness their potential. With the advent of powerful machine learning (ML) methods, analysis of complex multisource data became feasible. Nevertheless, applying feature selection methods is crucial to eliminate re-dundant and irrelevant data, improving the output accuracy. Unlike knee osteoarthritis, which has been extensively studied using ML models, TMJ OA remains an underexplored area. Therefore, we aimed to 1) Develop a reliable prediction tool for TMJ OA progression and identify the con-tributing factors during a 2–3-year follow-up period, 2) Develop a comprehensive prediction tool tailored for TMJ OA diagnosis and use explainable methods to identify key factors driving diag-nosis, and 3) Investigate the feasibility of privileged learning in addressing missing data when diagnosing TMJ OA. We successfully developed an open-source tool which combined 18 feature selection and ML methods. This allowed for the prediction of disease progression with an accuracy=0.87, area un-der the ROC curve (AUC)=0.72, and an F1 score=0.82. Using the interpretable SHAP analysis method, we identified the strongest predictors for TMJ OA progression. These included: clinical (headache, lower back pain, restless sleep), quantitative imaging (condyle high-grey-level-run-emphasis (HGLRE), articular fossa GL-non-uniformity, and long-run-low-GLRE, joint space), and biological markers in saliva (Osteoprotegerin, Angiogenin, VEGF, and MMP-7) and serum samples (ENA-78). Utilizing clinical, CBCT imaging, and biological data from 162 prospectively recruited subjects, we evaluated 77 ML methods. Random forest demonstrated the best diagnostic performance, achieving AUC=0.90, accuracy=0.79, precision=0.80, and F1=0.80. The integration of clinical, imaging, and biological markers enhanced TMJ OA diagnosis. The top contributing features were clinical (headache, restless sleep, mouth opening, muscle soreness), objective quantitative imag-ing (condyle Cluster-Prominence, HGLRE, SRHGLRE, Trabecular Thickness), and biological markers in saliva (TGFB-1, TRANCE, TIMP-1, PAI-1, VECadherin, CXCL-16) and serum (An-giogenin, PAI-1, VEGF, TRANCE, TIMP-1, BDNF, VECadherin). Lastly, we developed the KRVFL+ diagnostic tool, which can be used when only clinical and imaging data are available. It achieved an AUC, specificity, and precision of 0.81, 0.79, and 0.77, respectively. Collectively, these efforts emphasize the immense potential of multi-source data and ML applica-tions in presenting solutions for predicting TMJ OA progression and diagnosis, with potential implications for timely interventions and a transformative impact on TMJ OA healthcare deliv-ery.