Agreement analysis between examiner and artificial intelligence-derived measurements of condylar angles on cone-beam computed tomography scans using a semi-automated U-Net workflow

Accurate assessment of condylar angulation in temporomandibular joint (TMJ) analysis is essential for detecting morphological abnormalities.Manual evaluation on cone-beam computed tomography (CBCT) scans is time-consuming and examiner dependent, highlighting the potential role of artificial intelligence (AI).This study investigated the agreement between examiner and AI-derived condylar angle measurements using a semi-automated workflow.CBCT scans from 20 patients (40 condyles) were retrospectively analyzed.A total of 902 axial images from 10 patients were used for U-Net model training and validation, while 125 images from 2 patients were reserved for independent testing.Reliability and agreement analyses were then performed on the full dataset comprising 20 patients.The U-Net model achieved strong segmentation performance (Dice coefficient=0.891±0.056;intersection over union=0.807±0.073).Intra-examiner reliability was high (intraclass correlation coefficient=0.804).AI-derived condylar angles demonstrated moderate agreement with manual measurements.Bland-Altman analysis revealed a mean bias of 1.08°, with 74.1% of AI-derived values within ±5° of examiner measurements.Correlation analysis confirmed strong linear and rank associations, though systematic biases were observed.These findings support the feasibility of examiner-AI collaborative workflows to reduce subjectivity and improve reproducibility in TMJ assessment.They also emphasize the importance of reference plane standardization and multiinstitutional validation to establish robust clinical applicability.