Applying deep learning models to MRI scans of acute stroke patients to extract features indicative of functional outcome could assist a clinician’s treatment decisions. Here, we trained convolutional neural network models on ADC maps from hyper-acute ischemic stroke patients to predict 3-month mRS and used an interpretability technique to highlight regions in the ADC maps that were most important in the prediction of good and poor outcomes. Although the models had poor predictive power, the visual explanations supported our previous findings that predictions might be based not on ischemic regions, but on other relevant information inherent in the image.
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