Deep learning-based diagnosis of temporomandibular joint osteoarthritis using whole-body bone scans

Abstract

Temporomandibular joint osteoarthritis (TMJ-OA) is a degenerative condition that causes pain and functional limitation, yet its relationship with systemic osteoarthritis (OA) remains unclear. This study developed deep learning models to automatically diagnose TMJ-OA using bone scintigraphy (bone scans) and to evaluate systemic OA features as potential predictors. A dataset of 1,943 patients (3,886 TMJs) was analyzed with three convolutional neural network (CNN) approaches based on the VGG16 architecture. In head-and-neck imaging, the VGG16-Lite model achieved outstanding diagnostic accuracy (AUC >0.90) across age and sex subgroups, outperforming pretrained models. Whole-body scans excluding the head and neck provided only modest predictive value for TMJ-OA (AUC ∼0.65), suggesting limited utility of systemic features alone. These findings highlight the value of targeted bone scans with lightweight deep learning models for robust and efficient TMJ-OA detection, while also underscoring the need for further research into systemic associations.