학술논문
Head CT deep learning model is highly accurate for early infarct estimation
Document Type
Original Paper
Author
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.
Source
Scientific Reports. 13(1)
Subject
Language
English
ISSN
2045-2322
Abstract
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.