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

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.

Online Automatic Diagnosis System of Cardiac Arrhythmias Based on MIT-BIH ECG Database

Arrhythmias are a relatively common type of cardiovascular disease. Most cardiovascular diseases are often accompanied by arrhythmias. In clinical practice, an electrocardiogram (ECG) can be used as a primary diagnostic tool for cardiac activity and is commonly used to detect arrhythmias. Based on the hidden and sudden nature of the MIT-BIH ECG database signal and the small-signal amplitude, this paper constructs a hybrid model for the temporal correlation characteristics of the MIT-BIH ECG database data, to learn the deep-seated essential features of the target data, combine the characteristics of the information processing mechanism of the arrhythmia online automatic diagnosis system, and automatically extract the spatial features and temporal characteristics of the diagnostic data. First, a combination of median filter and bandstop filter is used to preprocess the data in the ECG database with individual differences in ECG waveforms, and there are problems of feature inaccuracy and useful feature omission which cannot effectively extract the features implied behind the massive ECG signals. Its diagnostic algorithm integrates feature extraction and classification into one, which avoids some bias in the feature extraction process and provides a new idea for the automatic diagnosis of cardiovascular diseases. To address the problem of feature importance variability in the temporal data of the MIT-BIH ECG database, a hybrid model is constructed by introducing algorithms in deep neural networks, which can enhance its diagnostic efficiency.