A machine learning AI model for predicting condylar erosion in patients with temporomandibular joint disorders and analyzing factors contributing to prediction.

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Abstract

In temporomandibular joint osteoarthritis, condylar erosion is considered a sign of disease progression. Few studies have clarified the extent to which clinical and imaging findings are associated with erosion. The purpose of this study was to develop a machine learning model to predict condylar erosion and identify the parameters contributing to prediction. We enrolled 197 patients (394 joints) in this study. Condylar erosion was determined using magnetic resonance imaging (MRI) and panoramic radiographs. Clinical data included age, sex, duration of symptoms, maximum mouth opening, joint sounds, temporomandibular joint pain, and masticatory muscle pain. MRI findings were evaluated for disc shape and position, signal changes at the posterior disc attachment (PDA), joint effusion, bone edema, condylar position, and limited movement. Erosion prediction was performed using clinical and MRI data with Prediction One and random forest models. Area under the receiver operating characteristic curve (AUC), accuracy, sensitivity, and specificity were calculated. Parameters contributing to prediction were analyzed. Prediction One showed AUC 0.845, accuracy 0.779, sensitivity 0.777, and specificity 0.780. Random forest had AUC 0.836, accuracy 0.759, sensitivity 0.777, and specificity 0.753. The top five parameters contributing to prediction in Prediction One were signal changes at PDA, disc position, bone edema, disc shape, and joint effusion; in random forest, these were disc position, joint effusion, signal changes at PDA, bone edema, and disc shape. We developed a highly accurate model for predicting condylar erosion and identified those parameters contributing to the prediction.

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