A Study on 3D Deep Learning-Based Automatic Diagnosis of Nasal Fractures
- Authors
- Seol, Yu Jin; Kim, Young Jae; Kim, Yoon Sang; Cheon, Young Woo; Kim, Kwang Gi
- Issue Date
- Jan-2022
- Publisher
- MDPI
- Keywords
- 3D-classification; Artificial intelligence; Computed aided diagnosis (CAD); Nasal fractures
- Citation
- Sensors, v.22, no.2
- Journal Title
- Sensors
- Volume
- 22
- Number
- 2
- URI
- https://scholarworks.bwise.kr/gachon/handle/2020.sw.gachon/83435
- DOI
- 10.3390/s22020506
- ISSN
- 1424-8220
- Abstract
- This paper reported a study on the 3-dimensional deep-learning-based automatic diagnosis of nasal fractures. (1) Background: The nasal bone is the most protuberant feature of the face; therefore, it is highly vulnerable to facial trauma and its fractures are known as the most common facial fractures worldwide. In addition, its adhesion causes rapid deformation, so a clear diagnosis is needed early after fracture onset. (2) Methods: The collected computed tomography images were reconstructed to isotropic voxel data including the whole region of the nasal bone, which are represented in a fixed cubic volume. The configured 3-dimensional input data were then automatically classified by the deep learning of residual neural networks (3D-ResNet34 and ResNet50) with the spatial context information using a single network, whose performance was evaluated by 5-fold cross-validation. (3) Results: The classification of nasal fractures with simple 3D-ResNet34 and ResNet50 networks achieved areas under the receiver operating characteristic curve of 94.5% and 93.4% for binary classification, respectively, both indicating unprecedented high performance in the task. (4) Conclusions: In this paper, it is presented the possibility of automatic nasal bone fracture diagnosis using a 3-dimensional Resnet-based single classification network and it will improve the diagnostic environment with future research. © 2022 by the authors. Licensee MDPI, Basel, Switzerland.
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