Automated Computer Evaluation of Acute Ischemic Stroke and Large Vessel Occlusion
This work addresses the critical need for timely diagnosis of stroke-related large vessel occlusion to improve patient outcomes, representing an incremental advancement in medical imaging analysis.
The paper tackled the problem of early identification of large vessel occlusion in acute ischemic stroke patients using hierarchical models combining demographic, clinical, and CT imaging features, achieving a sensitivity of 0.930, specificity of 0.684, accuracy of 0.790, and AUC of 0.850 on testing data.
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.