Michihito Nozawa1,2, Hirokazu Ito3, Yoshiko Ariji1, Motoki Fukuda1, Chinami Igarashi3, Masako Nishiyama1, Nobumi Ogi4, Akitoshi Katsumata5, Kaoru Kobayashi3, Eiichiro Ariji1. 1. Department of Oral and Maxillofacial Radiology, Aichi Gakuin University School of Dentistry, Nagoya, Japan. 2. Division of Radiology, Department of Oral Diagnostic Sciences, Showa University School of Dentistry, Tokyo, Japan. 3. Department of Oral and Maxillofacial Radiology, Tsurumi University School of Dentistry, Yokohama, Japan. 4. Department of Oral and Maxillofacial Surgery, Aichi Gakuin University School of Dentistry, Nagoya, Japan. 5. Department of Oral Radiology, Asahi University School of Dentistry, Mizuho, Japan.
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.
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.
Entities:
Keywords:
Artificial intelligence; Deep learning; Magnetic resonance imaging; Temporomandibular joint disc
Authors: Martin J Willemink; Wojciech A Koszek; Cailin Hardell; Jie Wu; Dominik Fleischmann; Hugh Harvey; Les R Folio; Ronald M Summers; Daniel L Rubin; Matthew P Lungren Journal: Radiology Date: 2020-02-18 Impact factor: 11.105