Background: Evaluating the extent of ischemic change is an important step in deciding whether to use thrombolysis or mechanical thrombectomy, but the current standard method, Alberta Stroke Program Early CT Score, is semiquantitative and has low consistency among raters. We aim to create and test a fully automated machine learning-based ischemic core segmentation model using only noncontrast-enhanced computed tomography images.
Methods: In this multicenter retrospective study, patients with anterior circulation acute ischemic stroke who received both computed tomography (CT) and magnetic resonance imaging before thrombolysis or recanalization treatment between 2013 and 2019 were included. On CT, the ischemic core was manually delineated using the diffusion-weighted image and apparent diffusion coefficient maps. A deep learning-based ischemic core segmentation model (DL model) was developed using data from 3 institutions (n=272), and the model performance was validated using data from 3 institutions (n=106 Results: The median time ).between CT and magnetic resonance imaging in the validation cohort was 18 min. The DL model calculated ischemic core volume was significantly correlated with the reference standard (intraclass correlation coefficient, 0.90, P<0.01). Both the early time window (≤4.5 hours from onset; intraclass correlation coefficient, 0.90, P<0.01) and the late time window (>4.5 hours from onset; intraclass correlation coefficient, 0.93, P<0.01) had significant correlations. The median difference in ivolume between the model and the reference standard was 4.7 mL (interquartile range, 0.8-12.4 mL). The DL model performed well in distinguishing large ischemic cores (>70 mL), with a sensitivity of 84.2%, specificity of 97.7%, and area under the curve of 0.91.
Conclusions: The deep learning-based ischemic core segmentation model, which was based on noncontrast-enhanced CT, demonstrated high accuracy in assessing ischemic core volume in patients with anterior circulation acute ischemic stroke.
Keywords: ischemic stroke; machine learning; magnetic resonance imaging; thrombectomy; tomography.