Abstract
Acute ischemic stroke is a disease with multiple etiologies. Classifying the mechanism of acute ischemic stroke is therefore fundamental for treatment and secondary prevention. The TOAST classification is currently the most widely-used system, but it has limitations of often classifying unknown causes and inadequate inter-rater reliability. Therefore, we tried to develop a 3D-convolutional neural network(CNN) based algorithm for stroke lesion segmentation and subtype classification using only diffusion and adc information of acute ischemic stroke patients. The recruited patients were 2251 patients with acute ischemic stroke who visited Chungbuk National University Hospital from February 2013 – July 2019. Our segmentation model for lesion segmentation in the training set achieved a Dice score of 0.843±0.009. The subtype classification model achieved an average accuracy of 81.9%, and each subtype was Large artery astherosclerosis (LAA) = 81.6%, Cardioembolism (CE) = 86.8%, Small vessel occlusion (SVO) = 72.9%, and Control = 86.3%. In conclusion, the proposed method shows great potential for identification of diffusion lesion segmentation and stroke subtype classification. As deep learning systems gradually develop, it would be useful in clinical practice and application.