Machine Learning-Based Outcome Prediction of Acute Ischemic Stroke After Endovascular Treatment

This paper presents a convolutional neural network (CNN) model, underpinned by deep learning principles, tailored to predict endovascular intervention outcomes in patients with acute anterior circulation ischemic stroke. Recognizing the crucial role of predictive models in guiding patient prognosis and treatment selection, we partitioned our dataset into training and testing subsets. The proposed CNN model exhibited outstanding performance during its training phase, reaching a stellar accuracy rate of 100%. This performance was maintained during the testing phase, with an accuracy surpassing 94.12%. Such minimal deviation between training and testing accuracies underscores the robustness of our model and suggests a reduced likelihood of overfitting, enhancing its relevance for real-world clinical scenarios. These promising results signify a leap forward in harnessing the power of deep learning for medical prognostication, particularly in stroke intervention outcomes.