Atherosclerotic carotid artery stenosis is a major cause of stroke, and early identification of high-risk patients combined with surgical intervention can significantly reduce stroke risk. Currently, stroke risk assessment in patients with carotid artery stenosis primarily relies on imaging indicators such as plaque morphology, composition, and degree of stenosis, with less emphasis on indicators directly related to inflammation, hemodynamics, and plaque instability. Certain circulating metabolites are closely linked to plaque progression and are direct risk factors for stroke. However, there is a lack of stroke risk prediction models for patients with carotid stenosis that incorporate these indicators, and the ability to identify high-risk patients needs improvement.

This study proposes using deep learning technology to integrate multidimensional data from plaque imaging, fluid dynamics, circulating metabolomics, and proteomics to construct an accurate prediction model for cerebrovascular events in patients with carotid artery stenosis. Additionally, it aims to explore markers of plaque instability characteristics based on plaque pathology. The study is expected to provide a basis for identifying high-risk patients with carotid artery stenosis, thereby laying the foundation for reducing stroke risk and improving long-term patient outcomes.

Inclusion Criteria:

• Patients diagnosed with atherosclerotic carotid stenosis;
• Those who signed informed consent and volunteered to participate in this study.

Exclusion Criteria:

• Patients with combined severe stenosis of intracranial arteries;
• Patients with combined cardiogenic embolism, vasculitis, entrapment aneurysm and other related cerebral infarction;
• Combination of tumors, rheumatic immune system diseases, hematologic diseases and other diseases that change blood metabolism and proteomic characteristics;
• There are contraindications to magnetic resonance scanning and allergy to gadolinium contrast agent;
• Inability to cooperate in completing the relevant examinations.

All patients who met the enrollment criteria at Beijing Tiantan Hospital.