Keywords: Stroke, Nervous system
Motivation: Acute ischemic stroke(AIS) is associated with high rates of disability and mortality, there are currently no reliable methods for early prediction of poor prognosis in AIS.
Goal(s): Exploring the value of radiomics and deep learning based on multimodal MRI in predicting poor prognosis in Acute ischemic stroke.
Approach: This study combines the Clinic Model, Radiomics Model, and Deep Learning Model to develop the CRD Model (Clinic-Radiomics-Deep Learning). The predictive efficacy of each model for poor prognosis is evaluated using receiver operating characteristic curves.
Results: The CRD model, based on multimodal MRI, demonstrates high diagnostic efficacy and reliability in predicting poor prognosis in AIS
Impact: These findings suggest that CRD model holds considerable potential for aiding clinicians in risk assessment and decision-making for AIS patients.
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