Development of a diagnostic model for acute ischemic stroke early identification based on SE-ResNeXt

Background Acute ischemic stroke (AIS) is a disease with high incidence rate and mortality. At present, the accuracy of AIS recognition based on Non contrast computed tomography (NCCT) images is not sufficient to meet clinical needs. We hope to develop and validate an AIS recognition model that can achieve timely and accurate recognition. Methods We retrospectively collected NCCT images of 287 patients from the Second Affiliated Hospital of Zhejiang University School of Medicine, and randomly divided them into a training set n = 230 and a testing set n = 57 according to a ratio of 8:2. We developed a deep learning AIS recognition model based on 3D SE-ResNeXt. The classification performance of the model was evaluated using the area under the receiver operating characteristic (ROC) curve (AUC), accuracy, sensitivity, specificity, and F1 score. Results The AUC, accuracy, sensitivity, specificity, and F1 of the model on the training set were 0.96, 0.94, 0.91, 0.94, and 0.92, respectively. The AUC, accuracy, sensitivity, specificity, and F1 on the test set were 0.90, 0.88, 0.82, 0.86, and 0.84, respectively. Compared with other deep learning models, the model used in this article has the best performance. Conclusion These results indicate that the proposed method can achieve early identification of acute ischemic stroke on NCCT images, which has high clinical significance.