A cute stroke care changed dramatically in 2015 with the publication of several randomized control trials demonstrating that endovascular therapy is more effective than alteplase alone for large vessel occlusion (LVO) stroke (1-7). Furthermore, the endovascular therapy treatment effect is profoundly time dependent, and every minute that we work faster to achieve vessel recanalization we can provide the gift of a week of disability-free life to patients (8). LVO stroke is a medical diagnosis that cannot be missed and must be made quickly. One of the commonly used methods to confirm or exclude the presence of a LVO quickly is with CT angiography, a 3-minute examination that can easily be performed following the noncontrast head CT that is standard of care for all acute stroke imaging (9-11). Multiphase CT angiography is a protocol recently introduced for acute stroke imaging that aims to both improve LVO detection and improve patient selection for endovascular therapy (12-14). Recent advances in deep learning, a class of machine learning, inspired new research on the uses of convolutional neural networks to perform at high levels in computer vision problems (15). These architectures hold great promise for enhancing the workflow in radiology. Specifically, in the neuroradiology domain, deep learning has been used to segment microhemorrhages at MRI with a sensitivity greater than 93% (16), to automate identification and to segment ischemic brain lesions on diffusion-weighted MRI scans (17), to detect early ischemic infarct at noncontrast CT with precision similar to diffusion-weighted MRI (18), and to detect intracranial hemorrhage at CT by reducing the time to diagnosis by 96% (19).
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