Automatic segmentation of the temporomandibular joint disc on magnetic resonance images using a deep learning technique
Michihito Nozawa, Hirokazu Ito, Yoshiko Ariji, Motoki Fukuda, Chinami Igarashi, Masako Nishiyama, Nobumi Ogi, Akitoshi Katsumata, Kaoru Kobayashi, Eiichiro Ariji
Abstract
OBJECTIVES: The aims of the present study were to construct a deep learning model for automatic segmentation of the temporomandibular joint (TMJ) disc on magnetic resonance (MR) images, and to evaluate the performances using the internal and external test data. METHODS: In total, 1200 MR images of closed and open mouth positions in patients with temporomandibular disorder (TMD) were collected from two hospitals (Hospitals A and B). The training and validation data comprised 1000 images from Hospital A, which were used to create a segmentation model. The performance was evaluated using 200 images from Hospital A (internal validity test) and 200 images from Hospital B (external validity test). RESULTS: Although the analysis of performance determined with data from Hospital B showed low recall (sensitivity), compared with the performance determined with data from Hospital A, both performances were above 80%. Precision (positive predictive value) was lower when test data from Hospital A were used for the position of anterior disc displacement. According to the intra-articular TMD classification, the proportions of accurately assigned TMJs were higher when using images from Hospital A than when using images from Hospital B. CONCLUSION: The segmentation deep learning model created in this study may be useful for identifying disc positions on MR images.