Artificial intelligence-enhanced diagnosis of degenerative joint disease using temporomandibular joint panoramic radiography and joint noise data.

ABSTRACT: This study aimed to develop an artificial intelligence (AI) model for the screening of degenerative joint disease (DJD) using temporomandibular joint (TMJ) panoramic radiography and joint noise data. A total of 2631 TMJ panoramic images were collected, resulting in a final dataset of 3908 images (2127 normal (N) and 1781 DJD (D)) after excluding indeterminate cases and errors. AI models using GoogleNet were evaluated with six different combinations of image data, clinician-detected crepitus, and patient-reported joint noise. The model that integrated all joint noise data with imaging demonstrated the highest performance, achieving an F1-score of 0.72. Another model, which incorporated both imaging and crepitus, also achieved the same F1-score but had lower D recall (0.55 vs. 0.67) and N pr

SUBMITTER: Choi E 

PROVIDER: S-EPMC11730964 | biostudies-literature | 2025 Jan

REPOSITORIES: biostudies-literature