Hybrid CNN-ViT Modeling For Predicting Functional Outcome After Ischemic Stroke: A Retrospective Study

Acute ischemic stroke (AIS) is a major cause of death and long-term disability, and early prediction of functional recovery is important for guiding clinical decision-making and rehabilitation planning. Functional outcome at 90 days is commonly assessed using the modified Rankin Scale (mRS), but predicting long-term outcome from early clinical and imaging information remains challenging. We hypothesized that integrating multiparametric magnetic resonance imaging (MRI) with structured clinical variables using a hybrid convolutional neural network (CNN)-Vision Transformer (ViT) architecture would improve prediction of 90-day functional outcomes compared with single-modality models. A retrospective cohort of 300 AIS patients who underwent multiparametric MRI was analyzed and divided into a training-validation cohort (n = 250) and an internal independent test cohort (n = 50). An additional external test cohort of 37 AIS patients was included to assess model generalizability. A hybrid CNN-ViT model was developed to extract multiparametric MRI features, and imaging and clinical predictions were integrated using stacked logistic regression. Model performance was evaluated using the area under the receiver operating characteristic curve (AUC), sensitivity, specificity, and accuracy. Among the evaluated clinical models, the support vector machine achieved the highest internal test AUC (0.878). The imaging model achieved an AUC of 0.782. The multimodal fusion model achieved the best overall internal performance, with an AUC of 0.885, sensitivity of 0.920, specificity of 0.760, and accuracy of 0.840. Similar performance trends were observed in the external test cohort. These findings suggest that stacked fusion of multiparametric MRI and clinical predictions may improve 90-day functional outcome prediction after AIS. However, larger multicenter validation studies are required before clinical implementation.