Yuan-Li Zhang

Development and validation of an interpretable machine learning model integrating baseline multimodal CT perfusion and clinical data for predicting 9-month functional outcomes in acute ischemic stroke

Background and purpose Accurate early prediction of long-term functional outcomes in acute ischemic stroke (AIS) remains challenging. We aimed to develop and validate an interpretable machine learning model integrating baseline multimodal CT perfusion and clinical data to predict 9-month poor functional outcome [modifi...