Predicting Osteoarthritis of the Temporomandibular Joint Using Random Forest with Privileged Information.

Osteoarthritis of the temporomandibular joint (TMJ OA) is the most common disorder of the TMJ. A clinical decision support (CDS) system designed to detect TMJ OA could function as a useful screening tool as part of regular check-ups to detect early onset. This study implements a CDS concept model based on Random Forest and dubbed RF to predict TMJ OA with the hypothesis that a model which leverages high-resolution radiological and biomarker data in training only can improve predictions compared with a baseline model which does not use privileged information. We found that the RF model can outperform the baseline model even when privileged features are not of gold standard quality. Additionally, we introduce a novel method for post-hoc feature analysis, finding shortRunHighGreyLevelEmphasis of the lateral condyles and joint distance to be the most important features from the privileged modalities for predicting TMJ OA.