Osteoarthritis of the Temporomandibular Joint can be diagnosed earlier using biomarkers and machine learning
The study found that a combination of clinical features (headaches, range of mouth opening without pain), radiomics features (entropy, energy, Haralick correlation), and interactions between biomolecular markers and clinical features could accurately predict temporomandibular joint osteoarthritis (TMJ OA) status. Using a machine learning model combining XGBoost and LightGBM, the study achieved an accuracy of 82.3% in diagnosing TMJ OA.