This study investigated the usefulness of deep learning-based automatic detection of anterior disc displacement (ADD) from magnetic resonance imaging (MRI) of patients with temporomandibular joint disorder (TMD). Sagittal MRI images of 2520 TMJs were collected from 861 men and 399 women (average age 37.33 ± 18.83 years). A deep learning algorithm with a convolutional neural network was developed. Data augmentation and the Adam optimizer were applied to reduce the risk of overfitting the deep-learning model. The prediction performances were compared between the models and human experts based on areas under the curve (AUCs). The fine-tuning model showed excellent prediction performance (AUC = 0.8775) and acceptable accuracy (approximately 77%). Comparing the AUC values of the from-scratch (0.8269) and freeze models (0.5858) showed lower performances of the other models compared to the fine-tuning model. In Grad-CAM visualizations, the fine-tuning scheme focused more on the TMJ disc when judging ADD, and the sparsity was higher than that of the from-scratch scheme (84.69% vs. 55.61%, p < 0.05). The three fine-tuned ensemble models using different data augmentation techniques showed a prediction accuracy of 83%. Moreover, the AUC values of ADD were higher when patients with TMD were divided by age (0.8549–0.9275) and sex (male: 0.8483, female: 0.9276). While the accuracy of the ensemble model was higher than that of human experts, the difference was not significant (p = 0.1987–0.0671). Learning from pre-trained weights allowed the fine-tuning model to outperform the from-scratch model. Another benefit of the fine-tuning model for diagnosing ADD of TMJ in Grad-CAM analysis was the deactivation of unwanted gradient values to provide clearer visualizations compared to the from-scratch model. The Grad-CAM visualizations also agreed with the model learned through important features in the joint disc area. The accuracy was further improved by an ensemble of three fine-tuning models using diversified data. The main benefits of this model were the higher specificity compared to human experts, which may be useful for preventing true negative cases, and the maintenance of its prediction accuracy across sexes and ages, suggesting a generalized prediction.

The aim of this study is to investigate the relationship of 18 radiomorphometric parameters of panoramic radiographs based on age, and to estimate the age group of people with permanent dentition in a non-invasive, comprehensive, and accurate manner using five machine learning algorithms. For the study population (209 men and 262 women; mean age, 32.12 ± 18.71 years), 471 digital panoramic radiographs of Korean individuals were applied. The participants were divided into three groups (with a 20-year age gap) and six groups (with a 10-year age gap), and each age group was estimated using the following five machine learning models: a linear discriminant analysis, logistic regression, kernelized support vector machines, multilayer perceptron, and extreme gradient boosting. Finally, a Fisher discriminant analysis was used to visualize the data configuration. In the prediction of the three age-group classification, the areas under the curve (AUCs) obtained for classifying young ages (10–19 years) ranged from 0.85 to 0.88 for five different machine learning models. The AUC values of the older age group (50–69 years) ranged from 0.82 to 0.88, and those of adults (20–49 years) were approximately 0.73. In the six age-group classification, the best scores were also found in age groups 1 (10–19 years) and 6 (60–69 years), with mean AUCs ranging from 0.85 to 0.87 and 80 to 0.90, respectively. A feature analysis based on LDA weights showed that the L-Pulp Area was important for discriminating young ages (10–49 years), and L-Crown, U-Crown, L-Implant, U-Implant, and Periodontitis were used as predictors for discriminating older ages (50–69 years). We established acceptable linear and nonlinear machine learning models for a dental age group estimation using multiple maxillary and mandibular radiomorphometric parameters. Since certain radiomorphological characteristics of young and the elderly were linearly related to age, young and old groups could be easily distinguished from other age groups with automated machine learning models.

Aim: This study aimed to investigate the usefulness of deep-learning-based automatic detection of anterior disc displacement (ADD) in patients with temporomandibular joint disorder (TMD) using magnetic resonance imaging (MRI). Methods: Sagittal MRI images of 2520 TMJs were collected from the study population (861 men, 399 women; average age 37.33±18.83 years). A deep learning algorithm with a convolutional neural network (CNN) was performed. Data augmentation and the Adam optimizer were applied to reduce the risk of overfitting the deep learning model. The prediction performance of the models and human experts was compared using area under the curve (AUC). Results: The prediction performance of the fine-tuning model was excellent with an AUC of 0.8775, with acceptable accuracy (0.83%). On comparing the AUC values of the from-scratch (0.8269) and freeze models (0.5858), the performances of the other models were lower than that of the fine-tuning model. Through Grad-CAM visualizations, the fine-tuning scheme focused more on the TMJ disc when judging ADD, and the sparsity was higher than that of the from-scratch scheme (84.69% vs. 55.61%, p-value <0.05). In the three fine-tuning ensembled models using different data augmentation techniques, the prediction accuracy was 0.8333. Moreover, the AUC values of ADD were higher when patients with TMD were divided by age group (0.8549–0.9275) and sex (male: 0.8483, female: 0.9276). The accuracy of the ensemble model was higher than that of the human experts; however, this difference was not significant (p-value: 0.1633–0.0519). Conclusion: Our CNN model had excellent and outstanding accuracy in detecting ADD and could potentially be used by clinicians to evaluate ADD on MR images of TMD patients and improve treatment outcomes.

Aim: The aim of this study is to investigate the relationship of 18 radiomorphological parameters of panoramic radiographs based on age, and to estimate the age group of people with permanent dentition in a non-invasive, comprehensive, and accurate manner using five machine learning algorithms.Methods: For the study population (209 men and 262 women; mean age, 32.12±18.71 years), 471 digital panoramic radiographs of Korean individuals were applied. The participants were divided into three groups (with a 20-year age gap) and six groups (with a 10-year age gap), and each age group was estimated using the following five machine learning models: a linear discriminant analysis (LDA), logistic regression, kernelized support vector machines, multilayer perceptron, and extreme gradient boosting. Finally, a Fisher discriminant analysis was used to visualize the data configuration. Results: In the prediction of the three age-group classification, the areas under the curve (AUCs) obtained for classifying young ages (10–19 y) ranged from 0.85 to 0.88 for five different machine learning models. The AUC values of the older age group (50–69 y) ranged from 0.82 to 0.88, and those of adults (20–49 y) were approximately 0.73. In the six age-group classification, the best scores were also found in age groups 1 (10–19 y) and 6 (60–69 y), with mean AUCs ranging from 0.85 to 0.87 and 80 to 0.90, respectively. A feature analysis based on LDA weights showed that the L-Pulp Area was important for discriminating young ages (10–49 y), and L-Crown, U-Crown, L-Implant, U-Implant, and Periodontitis were used as predictors for discriminating older ages (50–69 y). Conclusion: We established acceptable linear and nonlinear machine learning models for a dental age group estimation using multiple maxillary and mandibular radiomorphometric parameters.