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Abstract #2823

Automated Prediction of Stroke Lesion Outcome using Multiparametric Deep Neural Network

Zexin Yan1, Lian Ding2, Hongkun Yin3, Haiyan Lou4, and JUN YANG3

1School of Data and Computer Science, Sun-Yat Sen University, Guangzhou, China, 2Academy for Advanced Interdisciplinary Studies, Peking University, Beijing, China, 3YITU Healthcare, Shanghai, China, 4Department of Radiology, The First Affiliated Hospital, School of Medicine, Zhejiang University, Hangzhou, China

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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