Head CT Deep Learning Model for Early Stroke Identification Outperforms Human Experts

Abstract Non-contrast head CT (NCCT) is extremely insensitive for early (< 3-6hrs) acute infarct identification. We developed a deep learning model that detects and delineates early acute infarcts on NCCT, using diffusion MRI as ground truth (3,566 NCCT/MRI training pairs). The model substantially outperformed 3 expert neuroradiologists on a test set of 150 CT scans (sensitivity 96% model versus 61–66% experts); infarct volume estimates strongly correlated with those of diffusion MRI (r2 > 0.98).