Differentiating Between Brain Hemorrhage and Contrast

Develop a deep learning model to differentiate brain hemorrhage from contrast agent extravasation, and evaluate the model performance and generalization ability

The goal of this observational study is to use artificial intelligence to differentiate
 cerebral hemorrhage from contrast agent extravasation after mechanical revascularization in
 ischemic stroke.
 
 The main question it aims to answer is: Whether artificial intelligence can help
 differentiate brain hemorrhage from contrast agent extravasation.
 
 Patients with intracranial high-density lesions on CT scans within 24h after mechanical
 revascularization will be included. Expected to enroll 500 patients. The type of high-density
 lesion is determined according to dual-energy CT images or follow-up images. Patients will be
 divided into training group, validation and testing groups by stratified random sampling
 (6:2:2). After the images and the image labels are obtained, deep learning artificial
 intelligence will be used to learn the image characteristics and establish a diagnostic
 model, and the model performance and generalization ability will be evaluated.