  Large vessel occlusion (LVO) plays an important role in the diagnosis of
acute ischemic stroke. Identifying LVO of patients in the early stage on
admission would significantly lower the probabilities of suffering from severe
effects due to stroke or even save their lives. In this paper, we utilized both
structural and imaging data from all recorded acute ischemic stroke patients in
Hong Kong. Total 300 patients (200 training and 100 testing) are used in this
study. We established three hierarchical models based on demographic data,
clinical data and features obtained from computerized tomography (CT) scans.
The first two stages of modeling are merely based on demographic and clinical
data. Besides, the third model utilized extra CT imaging features obtained from
deep learning model. The optimal cutoff is determined at the maximal Youden
index based on 10-fold cross-validation. With both clinical and imaging
features, the Level-3 model achieved the best performance on testing data. The
sensitivity, specificity, Youden index, accuracy and area under the curve (AUC)
are 0.930, 0.684, 0.614, 0.790 and 0.850 respectively.
