COVID-19 has spread around the world since 2019. Approximately 6.5% of COVID-19 a risk of developing severe disease with high mortality rate. To reduce the mortality rate and provide appropriate treatment, this research established an integrated models with to predict the clinical outcome of COVID-19 patients with clinical, deep learning and radiomics features. To obtain the optimal feature combination for prediction, 9 clinical features combination was selected from all available clinical factors after using LASSO, 18 deep learning features from U-Net architecture, and9radiomics features from segmentation result. A total of 213 COVID-19 patients and 335 non-COVID-19 patients from5hospitals were enrolled and used as training and test sample in this research. The proposed model obtained an accuracy, precision, recall, specificity, F1-score and ROC curve of 0.971, 0.943, 0.937, 0.974, 0.941 and 0.979, respectively, which exceeds the related work using only clinical, deep learning or radiomics factors.
The kinematic and morphological abnormalities can be used for accurate detection of myocardial infarction without contrast agents. It has important implications for the early treatment of patients. However, methods based on motion tracking are time-consuming, and the complex movements of the heart make them difficult to implement. In this paper, we propose a deep learning constrained framework based on relative motion features. It can detect myocardial infarction areas through cine cardiac magnetic resonance imaging(CMRI) images. It includes one relative motion extraction component and one deep neural network component. In the relative motion model, a U-Net model is used to segment the myocardial contour. After that, the motion features and pixel features of the myocardium are extracted and fused. Finally, the extracted relative features are further learned via the deep neural network model based on ConvLSTM to predict the myocardial infarction area. Our method doesn’t need a pre-find position match and is more suitable for the physiological process of the myocardium. We validated the performance of our framework in 276 cine CMRI sequences datasets, and it yielded a high consistency with manual delineation (90.8% detection accuracy). The results demonstrate that our proposed method can be an attractive tool for the diagnosis of myocardial infarction in the clinic.
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