Automatic Prediction of TMJ Disc Displacement in CBCT Images Using Machine Learning.
Authors
Affiliations (6)
Affiliations (6)
- Department of Oral and Maxillofacial Radiology, College of Dentistry, Yonsei University, 50-1 Yonsei-Ro Seodaemun-Gu, Seoul, 03722, Korea.
- Institute for Innovation in Digital Healthcare, Yonsei University, Seoul, Korea.
- Oral Science Research Center, College of Dentistry, Yonsei University, Seoul, Korea.
- Department of Oral and Maxillofacial Radiology, College of Dentistry, Yonsei University, 50-1 Yonsei-Ro Seodaemun-Gu, Seoul, 03722, Korea. [email protected].
- Institute for Innovation in Digital Healthcare, Yonsei University, Seoul, Korea. [email protected].
- Oral Science Research Center, College of Dentistry, Yonsei University, Seoul, Korea. [email protected].
Abstract
Magnetic resonance imaging (MRI) is the gold standard for diagnosing disc displacement in temporomandibular joint (TMJ) disorders, but its high cost and practical challenges limit its accessibility. This study aimed to develop a machine learning (ML) model that can predict TMJ disc displacement using only cone-beam computed tomography (CBCT)-based radiomics features without MRI. CBCT images of 247 mandibular condyles from 134 patients who also underwent MRI scans were analyzed. To conduct three experiments based on the classification of various patient groups, we trained two ML models, random forest (RF) and extreme gradient boosting (XGBoost). Experiment 1 classified the data into three groups: Normal, disc displacement with reduction (DDWR), and disc displacement without reduction (DDWOR). Experiment 2 classified Normal versus disc displacement group (DDWR and DDWOR), and Experiment 3 classified Normal and DDWR versus DDWOR group. The RF model showed higher performance than XGBoost across all three experiments, and in particular, Experiment 3, which differentiated DDWOR from other conditions, achieved the highest accuracy with an area under the receiver operating characteristic curve (AUC) values of 0.86 (RF) and 0.85 (XGBoost). Experiment 2 followed with AUC values of 0.76 (RF) and 0.75 (XGBoost), while Experiment 1, which classified all three groups, had the lowest accuracy of 0.63 (RF) and 0.59 (XGBoost). The RF model, utilizing radiomics features from CBCT images, demonstrated potential as an assistant tool for predicting DDWOR, which requires the most careful management.