Journal of dentistry · 2026
An artificial intelligence-assisted automated quantitative evaluation system for condylar morphological changes of temporomandibular joint.
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Li Yuan, Liu Mu-Qing, Xu Zi-Neng, Ni Fang-Duan, Xiao Yang, Bai Hai-Long, Ding Peng, Fu Kai-Yuan, Lei Jie
Аннотация
OBJECTIVES: To develop an artificial intelligence (AI)-assisted system for the automated quantitative evaluation of temporomandibular joint (TMJ) condylar morphological changes.
METHODS: This study utilized 212 cone-beam computed tomography (CBCT) scans of the TMJ from 114 patients, with radiologist-validated annotations. The proposed system comprised three modules: (1) an automated segmentation model based on the U-Net architecture, trained on 120 TMJs (60 normal and 60 osteoarthritis condyles) and tested on 32 TMJs (16 normal, 8 type I osteoarthritis condyles with surface erosion/destruction, and 8 type II condyles with osteophyte formation, sclerosis, deformity, or cyst-like lesions); (2) an automated registration model using mutual information similarity, validated on 20 paired pre- and post-treatment TMJ scans of 10 patients with normal condyles; and (3) an automated quantitative measurement model for assessing condylar morphological changes based on anatomical landmarks. Segmentation accuracy was evaluated using the Dice similarity coefficient. Furthermore, pre- and post-treatment bilateral CBCT data from 10 patients with unilateral osteoarthritis were used to assess system accuracy. Consistency between automated and manual measurements was evaluated using the intra-class correlation coefficient. Additionally, time efficiency was assessed.
RESULTS: The Dice similarity coefficient values for 16 normal, 8 type I, and 8 type II osteoarthritis condyles were 0.988, 0.986, and 0.981, respectively. The intraclass correlation coefficients of condyle and condylar head volumetric changes between the automated and manual methods were 0.983 and 0.995, respectively. The automated system completed measurements in 85 seconds versus 100 minutes for manual measurement.
CONCLUSIONS: The AI-assisted quantitative evaluation system demonstrated initial technical feasibility on a single-center CBCT dataset, providing an intuitive, user-friendly, and efficient assessment of the radiographic manifestations of condylar morphological changes. However, further validation using diverse cases is required.
CLINICAL SIGNIFICANCE: The TMJ condyle demonstrates active bone remodeling capacity, which is significantly influenced by clinical interventions such as prosthodontic treatment, orthodontic therapy, orthognathic surgery, and TMJ-related therapies. The proposed AI-assisted system facilitates rapid, objective quantification and three-dimensional visualization of condylar morphological changes. This dual functionality enables clinicians to evaluate disease progression and treatment effects on condylar morphological changes from radiographic perspectives.
METHODS: This study utilized 212 cone-beam computed tomography (CBCT) scans of the TMJ from 114 patients, with radiologist-validated annotations. The proposed system comprised three modules: (1) an automated segmentation model based on the U-Net architecture, trained on 120 TMJs (60 normal and 60 osteoarthritis condyles) and tested on 32 TMJs (16 normal, 8 type I osteoarthritis condyles with surface erosion/destruction, and 8 type II condyles with osteophyte formation, sclerosis, deformity, or cyst-like lesions); (2) an automated registration model using mutual information similarity, validated on 20 paired pre- and post-treatment TMJ scans of 10 patients with normal condyles; and (3) an automated quantitative measurement model for assessing condylar morphological changes based on anatomical landmarks. Segmentation accuracy was evaluated using the Dice similarity coefficient. Furthermore, pre- and post-treatment bilateral CBCT data from 10 patients with unilateral osteoarthritis were used to assess system accuracy. Consistency between automated and manual measurements was evaluated using the intra-class correlation coefficient. Additionally, time efficiency was assessed.
RESULTS: The Dice similarity coefficient values for 16 normal, 8 type I, and 8 type II osteoarthritis condyles were 0.988, 0.986, and 0.981, respectively. The intraclass correlation coefficients of condyle and condylar head volumetric changes between the automated and manual methods were 0.983 and 0.995, respectively. The automated system completed measurements in 85 seconds versus 100 minutes for manual measurement.
CONCLUSIONS: The AI-assisted quantitative evaluation system demonstrated initial technical feasibility on a single-center CBCT dataset, providing an intuitive, user-friendly, and efficient assessment of the radiographic manifestations of condylar morphological changes. However, further validation using diverse cases is required.
CLINICAL SIGNIFICANCE: The TMJ condyle demonstrates active bone remodeling capacity, which is significantly influenced by clinical interventions such as prosthodontic treatment, orthodontic therapy, orthognathic surgery, and TMJ-related therapies. The proposed AI-assisted system facilitates rapid, objective quantification and three-dimensional visualization of condylar morphological changes. This dual functionality enables clinicians to evaluate disease progression and treatment effects on condylar morphological changes from radiographic perspectives.