Automatic detection of anteriorly displaced temporomandibular joint discs on magnetic resonance images using a deep learning algorithm

Abstract

Objectives: This study aimed to develop models that can automatically detect anterior disc displacement (ADD) of the temporomandibular joint (TMJ) on MRIs before orthodontic treatment to reduce the risk of developing serious complications after treatment.

Methods: We used 9009 sagittal MRI of the TMJ as input and constructed three sets of deep learning models to detect ADD automatically. Deep learning models were developed using a convolutional neural network (CNN) based on the ResNet architecture and the "Imagenet" database. Five-fold cross-validation, oversampling, and data augmentation techniques were applied to reduce the risk of overfitting the model. The accuracy and area under the curve (AUC) of the three models were compared.

Results: The performance of the maximum open mouth position model was excellent with accuracy and AUC of 0.970 (±0.007) and 0.990 (±0.005), respectively. For closed mouth position models, the accuracy and AUC of diagnostic Criteria 1 were 0.863 (±0.008) and 0.922 (±0.009), respectively significantly higher than that of diagnostic Criteria 2 with 0.839 (±0.013) (p = 0.009) and AUC of 0.885 (±0.018) (p = 0.003). The classification activation heat map also improved our understanding of the models and visually displayed the areas that play a key role in the model recognition process.

Conclusion: Our CNN model resulted in high accuracy and AUC in detecting ADD and can therefore potentially be used by clinicians to assess ADD before orthodontic treatment, and hence improve treatment outcomes.

Keywords: Deep Learning; Magnetic Resonance Imaging; Temporomandibular Joint Disc.

Figure 1.

Diagnostic criteria of ADD. Red arrows indicate the disc. White circles show the approximate outline of the articular eminence and the condyle head. Image a, image b and image c illustrate the ADD in diagnostic Criteria 1 close mouth, diagnostic Criteria 1 maximum open mouth and diagnostic Criteria 2 close mouth, respectively. ADD, anterior disc displacement.

Figure 2.

Overview of the study. ADD, anterior disc displacement.

Figure 3.

Receiver operating characteristic curves of three models. Image a, b and c for diagnostic criteria one close mouth, diagnostic criteria one maximum open mouth and diagnostic criteria two close mouth, respectively.

Figure 4.

Class activation maps. Rows: model of close mouth position diagnostic Criteria 1, model of maximum open mouth position diagnostic Criteria 1 and model of close mouth position diagnostic Criteria 2. Columns: classification (ADD, N-ADD). ADD, anterior disc displacement.