A Deep Learning-Based Automatic Collateral Assessment in Patients with Acute Ischemic Stroke

This study aimed to develop a supervised deep learning (DL) model for grading collateral status from dynamic susceptibility contrast magnetic resonance perfusion (DSC-MRP) images from patients with large vessel occlusion (LVO) acute ischemic stroke (AIS) and compare its performance against experts ’ manual grading. Among consecutive LVO-AIS at three medical center sites, DSC-MRP data were processed to generate collateral flow maps consisting of arterial, capillary, and venous phases. With the use of expert readings as a reference, a DL model was developed to analyze collateral status with o utput classified into good and poor grades. The resulting model was externally validated in a later-collected population from one medical center site. The model was trained on 255 patients and externally validated on 72 patients. In the all-site internal validation population, DL grading of good col lateral probability yielded a c statistic of 0.91; in the external validation population, thec statistic was 0.85. In the external validation population, there was moderate agreement between the experts ’ grades and DL grades (kappa = 0.53, 95% CI = 0.32–0.73,p <  0.0001). Day 7 infarct growth volume was higher in DL-graded poor collateral group than good collateral group patients (median volume [26 mL vs. 6 mL],p = 0.01) in patients with successful reperfusion (modified treatment in cerebral infarction (mTICI) = 2b–3). In all patients with a 90-day modified Ran...
Source: Translational Stroke Research - Category: Neurology Source Type: research