We evaluated deep learning algorithms’ segmentation of acute ischemic lesions on heterogeneous multi-center mutli-vendor clinical diffusion-weighted magnetic resonance imaging (MRI) data sets and explored the potential role of this tool for phenotyping acute ischemic stroke. The ensemble consisting of a mixture of data from 12 international stroke genetics research center  and single-center convolutional neural networks performed best, demonstrating the need for diverse training data sets for creating robust machine learning algorithms. 

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