Deep learning-based imaging model to predict early hematoma enlargement and hospital mortality in spontaneous intracerebral hemorrhage

Figure 1 A general flowchart of data analysis. A: Radiological features from hemorrhage and perihematomal edema non-contrast computed tomography images were extracted by the deep learning analysis and handcrafted radiomics analysis, respectively; B: The prediction models in identification of early enlargement of spontaneous intracerebral hemorrhage were approached via machine learning methods using radiological features; C: The prognostic model in prediction of hospital death took radiological features and the effect of hematoma expansion into account, and was visualized by nomogram. ROC: Receiver operating characteristic; SVM: Support vector machine.