Objective
The purpose of this research was to develop a deep-learning model to assess radiographic finger joint destruction in RA.
Methods
The model comprises two steps: a joint-detection step and a joint-evaluation step. Among 216 radiographs of 108 patients with RA, 186 radiographs were assigned to the training/validation dataset and 30 to the test dataset. In the training/validation dataset, images of PIP joints, the IP joint of the thumb or MCP joints were manually clipped and scored for joint space narrowing (JSN) and bone erosion by clinicians, and then these images were augmented. As a result, 11 160 images were used to train and validate a deep convolutional neural network for joint evaluation. Three thousand seven hundred and twenty selected images were used to train machine learning for joint detection. These steps were combined as the assessment model for radiographic finger joint destruction. Performance of the model was examined using the test dataset, which was not included in the training/validation process, by comparing the scores assigned by the model and clinicians.
Results
The model detected PIP joints, the IP joint of the thumb and MCP joints with a sensitivity of 95.3% and assigned scores for JSN and erosion. Accuracy (percentage of exact agreement) reached 49.3–65.4% for JSN and 70.6–74.1% for erosion. The correlation coefficient between scores by the model and clinicians per image was 0.72–0.88 for JSN and 0.54–0.75 for erosion.
Conclusion
Image processing with the trained convolutional neural network model is promising to assess radiographs in RA.
The standard 12-ECG are widely used in the diagnosis of arrhythmias and other cardiac disorders. Early and correct diagnosis of cardiac abnormalities can improve treatment results. However, manual interpretation of the electrocardiogram (ECG) is time-consuming and difficult to be scaled. Thus, automatic detection and classification of cardiac abnormalities can assist physicians in the diagnosis of the growing number of ECGs recorded. In recent years, deep neural networks (DNNs) have shown significant improvement in variety of tasks, including ECG classification. In this study, we attempt to classify 12-ECG PhysioNet/Computing in Cardiology Challenge 2020 data using DNN model. We adopt EfficientNet model, which achieved state-of-the-art result with ImageNet classification task, and modify model for ECG classification. During the training, we adopt data augmentation for ECG to improve the robustness of the model. With training data we achieve score of 0.585 using cross validation, relative improvement of 7.73% over model without data augmentation. We achieved a score of 0.456, but were not ranked due to omissions in the submission (Team name: NN-MIH).
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