In Scientific reports ; h5-index 158.0
Non-contrast head CT (NCCT) is extremely insensitive for early (< 3-6 h) acute infarct identification. We developed a deep learning model that detects and delineates suspected early acute infarcts on NCCT, using diffusion MRI as ground truth (3566 NCCT/MRI training patient pairs). The model substantially outperformed 3 expert neuroradiologists on a test set of 150 CT scans of patients who were potential candidates for thrombectomy (60 stroke-negative, 90 stroke-positive middle cerebral artery territory only infarcts), with sensitivity 96% (specificity 72%) for the model versus 61-66% (specificity 90-92%) for the experts; model infarct volume estimates also strongly correlated with those of diffusion MRI (r2 > 0.98). When this 150 CT test set was expanded to include a total of 364 CT scans with a more heterogeneous distribution of infarct locations (94 stroke-negative, 270 stroke-positive mixed territory infarcts), model sensitivity was 97%, specificity 99%, for detection of infarcts larger than the 70 mL volume threshold used for patient selection in several major randomized controlled trials of thrombectomy treatment.
Gauriau Romane, Bizzo Bernardo C, Comeau Donnella S, Hillis James M, Bridge Christopher P, Chin John K, Pawar Jayashri, Pourvaziri Ali, Sesic Ivana, Sharaf Elshaimaa, Cao Jinjin, Noro Flavia T C, Wiggins Walter F, Caton M Travis, Kitamura Felipe, Dreyer Keith J, Kalafut John F, Andriole Katherine P, Pomerantz Stuart R, Gonzalez Ramon G, Lev Michael H
2023-Jan-05
