Abstract To develop and validate a deep learning framework for simultaneous automated quantification of DWI-ASPECTS and infarct core volume in middle cerebral artery acute ischemic stroke (MCA-AIS), and to evaluate its clinical utility for severity stratification in multicenter settings. A cohort of 738 patients diagnosed with MCA-AIS from four centers was divided into a train set (n=408), a validation set (n=116), an internal test set (n=60), and an external test set (n=154). A 3D U-Net architecture was trained for simultaneous infarct segmentation and the Alberta Stroke Program Early CT Score (ASPECTS) region analysis. Model performance was compared against expert neuroradiologists using intraclass correlation coefficients (ICC) and Spearman correlation coefficient. Furthermore, we investigated the correlation between ASPECTS deduction frequency, ASPECTS score, core volume and the severity of MCA-AIS. The 3D U-Net model showed a high correlation with manual segmentation, achieving a Dice coefficient (DC) of 0. 801 and a Spearman correlation coefficient of 0. 988 for volume measurements in the external test set. In both the internal and external test sets, the automated ASPECTS by the deep learning (DL) model (Automated), manual ASPECTS by raters (Raters) and manual ASPECTS by raters on DWI images registered with the template (Ratersₜemplate) exhibited a strong correlation and excellent agreement. The cortical regions (M1-M6) were particularly relevant in patients classified into moderate-severe group. The threshold values for the mild group and moderate-severe group on receiver operating characteristic (ROC) curve analysis for Diffusion-Weighted Imaging-the Alberta Stroke Program early CT score (DWI-ASPECTS) and Core volume, were 6 and 27. 86 mL, respectively. The DL model demonstrated comparable performance to neuroradiologists’ evaluation, potentially serving as an ancillary tool for physicians in making urgent clinical decisions. The severity of MCA-AIS was significantly associated with the specific ASPETCS regions and core volume, which may aid in identifying moderate-severe MCA-AIS.
Wei et al. (Mon,) studied this question.