Interpretation of Magnetic Resonance Images of Temporomandibular Joint Disorders by Using Deep Learning

Özsarı Ş., Yapicioglu F. R., Yilmaz D., Kamburoglu K., Guzel M. S., Bostanci G. E., ...More

IEEE ACCESS, vol.11, pp.49102-49113, 2023 (SCI-Expanded) identifier identifier

  • Publication Type: Article / Article
  • Volume: 11
  • Publication Date: 2023
  • Doi Number: 10.1109/access.2023.3277756
  • Journal Name: IEEE ACCESS
  • Journal Indexes: Science Citation Index Expanded (SCI-EXPANDED), Scopus, Compendex, INSPEC, Directory of Open Access Journals
  • Page Numbers: pp.49102-49113
  • Keywords: Deep learning, magnetic resonance imaging, temporomandibular joint disorders
  • Ankara University Affiliated: Yes

Abstract

In recent years, Machine Learning (ML), especially Deep Learning (DL) approaches, has attracted great attention in medical field. In this study, we proposed a deep learning-based approach in order to automatically diagnose Temporomandibular Disorder (TMD) on Magnetic Resonance (MR) images. 2576 MR images of 200 patients diagnosed with and without TMD were collected. These images were classified as 8 groups. First of all, a basic Convolutional Neural Network (CNN) was used for the problem. After that, 6 different fine-tuned pre-trained convolutional neural network models, Xception, ResNet-101, MobileNetV2, InceptionV3, DenseNet-121 and ConvNeXt were applied on data set. Finally, the accomplishment of Vision Transformer (ViT) in task solving was also discussed. Performances of the approaches were evaluated by metrics such as accuracy rate, precision, sensitivity, F1-score, Negative Predictive Value (NPV), specificity, Area Under Curve (AUC) and kappa coefficient. Grad-CAM results of the best architectures for diagnostic examination were obtained. Intraclass Correlation Coefficients (ICC) value was computed to assess correlation between the models. According to the test results, deep learning-based architectures assessed were found to be successful in the diagnosis of TMD.