The purpose of this study was to demonstrate the feasibility of using deep learning algorithms for automatic classification of DWI-ASPECTS from patients with acute ischemic stroke. DWI data from 319 patients with acute anterior circulation stroke were used to train and validate recurrent residual convolutional neural network models for binary task of classifying low- vs high- DWI-ASPECTS. Our model produced the accuracy of 84.9 ± 1.5% and the AUC of 0.925 ± 0.009, suggesting that this algorithm may provide an important ancillary tool for clinicians in a time-sensitive assessment of DWI-ASPECTS from acute ischemic stroke patients.
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