Recent stroke trials raised a demand for triage decision intelligence of ischemic lesion progression. This study aimed to develop a multiparametric deep neural network to segment regions that predicted final infarct formation. The PWI-derived CBF, CBV, MTT and Tmax maps served as multi-channel inputs to algorithm training. We used a 2.5D U-Net to generate lesion segmentation. Our approach showed a good sensitivity and specificity with AUC of 0.868 in predicting the final lesions, and a comparable performance of DICE and IOU. In conclusion, we demonstrated feasibility for predicting tissue outcome in acute ischemic stroke with multiparametric deep learning algorithm
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