An artificial intelligence model for the radiographic diagnosis of osteoarthritis of the temporomandibular joint

Abstract

The interpretation of the signs of Temporomandibular joint (TMJ) osteoarthritis on cone-beam computed tomography (CBCT) is highly subjective that hinders the diagnostic process. The objectives of this study were to develop and test the performance of an artificial intelligence (AI) model for the diagnosis of TMJ osteoarthritis from CBCT. A total of 2737 CBCT images from 943 patients were used for the training and validation of the AI model. The model was based on a single convolutional network while object detection was achieved using a single regression model. Two experienced evaluators performed a Diagnostic Criteria for Temporomandibular Disorders (DC/TMD)-based assessment to generate a separate model-testing set of 350 images in which the concluded diagnosis was considered the golden reference. The diagnostic performance of the model was then compared to an experienced oral radiologist. The AI diagnosis showed statistically higher agreement with the golden reference compared to the radiologist. Cohen’s kappa showed statistically significant differences in the agreement between the AI and the radiologist with the golden reference for the diagnosis of all signs collectively (P = 0.0079) and for subcortical cysts (P = 0.0214). AI is expected to eliminate the subjectivity associated with the human interpretation and expedite the diagnostic process of TMJ osteoarthritis.

ChatGPT‐4 as auxiliary tool in the temporomandibular disorders diagnostic: An opinion

“…In a preliminary study, Reda et al 13 demonstrated that AI can effectively identify TMDs early by accessing records of other clinical cases reported in the literature, thus providing a possible diagnosis based on symptoms. Likewise, Talaat et al 14 investigated the capacity of AI models to identify potential risks of osteoarthritis in the temporomandibular joint (TMJ) using computed tomography (CT)-considered the gold standard for investigation. Interestingly, these authors found that AI exhibited a greater degree of diagnostic accuracy compared with professional radiologists, in terms of both symptoms and the presence of morphostructural alterations (p = 0.0079 and p = 0.0214, respectively).…”