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

• Published in: IEEE access 2023-01, Vol.11, p.1-1
• Main Authors: Ozsari, Sifa, Yapicioglu, Fatima Rabia, Yilmaz, Dilek, Kamburoglu, Kivanc, Guzel, Mehmet Serdar, Bostanci, Gazi Erkan, Acici, Koray, Asuroglu, Tunc
• Format: Article
• Language: English
• Subjects: Artificial neural networks; Biomedical imaging; Convolutional neural networks; Correlation coefficients; Deep learning; Joints; Machine learning; Magnetic resonance imaging; Medical imaging; Neural networks; Protons; Radiology; Temporomandibular joint disorders
• ISSN: 2169-3536; 2169-3536
• DOI: 10.1109/ACCESS.2023.3277756

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.