Automatic temporomandibular disc displacement diagnosis via deep learning

Temporomandibular joint (TMJ) disc displacement is a common condition that required magnetic resonance imaging (MRI) for diagnosis. However, it has occasionally been challenging for doctors to make a firm diagnosis based on TMJ MRI due to imaging concerns and the significant requirement for clinical competence. As a result, a Multimodal Stepped Attention Net (MSANet) was built in this study, and a deep learning network was used to train the assisted diagnosis AI model (TMJ MRI-Net). A total of 600 patients were recruited, including 1200 lateral joint MRI sequences, which are made up of eight consecutive images for each lateral joint. 11 sides of cases with poor image quality were excluded. MSANet combining multimodal technology and attention mechanism was proposed and designed for this study, which mainly included Area Detection Module and Feature Network. The whole experiment was designed according to stard standard. There is statistically significant difference between the least square mean of diagnosis time for the physicians (from TMJ, Orthodontics and General Dentistry) with AI (TMJ MRI-Net) assistance group (16.15, 95% CI:10.88–21.41) and the physician only group (21.01, 95%CI: 15.74–26.28). The AUC, sensitivity and specificity of patients whether is disc displacement and patients whether the disc displacement is with/without reduction for the physicians' diagnoses were all statistically improved by the assistance of AI. In addition, AUC and specificity were improved for three different specialties with the AI assistance. Meanwhile, AI can help to save reading time for physicians from all three departments, and the increment was statistically significant. To conclude, the AI-assisted strategy significantly improved the diagnostic accuracy of physicians (especially in General Dentistry) on anterior disc displacement in TMJ MRI and improve diagnostic efficiency.