Context (archaeology)

Linguistics

Philosophy

Geotechnical engineering

Support vector machine

Binary classification

Surgery

Face (sociological concept)

Computer science

Receiver operating characteristic

Geology

Paleontology

Voxel

Pattern recognition (psychology)

Feature (linguistics)

Sociology

Artificial intelligence

Orthodontics

Fracture (geology)

Medicine

Deep learning

Artificial neural network

Nasal bone

Facial bone

Machine learning

Social science

Sensors

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.

A Study on 3D Deep Learning-Based Automatic Diagnosis of Nasal Fractures