Cerebral small vessel disease (CSVD) accounts for 20% of ischemic strokes and is the most common cause of vascular cognitive impairment. Early identification of CSVD is critical for early intervention and improve clinical outcomes. Magnetic resonance imaging (MRI) may represent as a sensitive and robust tool to detect early changes in brain subtle structures and functions. The study is to investigate the comprehensive evaluation by using AI in early diagnosis and management of CSVD.

Cerebral small vessel disease (CSVD) is an important cause of stroke, cognitive impairment, and other diseases, and its early quantitative evaluation can significantly improve patient prognosis. Magnetic resonance imaging (MRI) is an important method to evaluate the occurrence, development, and severity of CSVD. However, the diagnostic process lacks quantitative evaluation criteria and is limited by experience, which may easily lead to missed diagnoses and misdiagnoses. Based on the current technical challenges, subject development and upgrade of knowledge, to avoid the occurrence of adverse medical accidents, simplify the diagnostic process, artificial intelligence（AI） has become the alternative method of choice, by constructing training deep learning model,which can assist doctors in clinical decision-making to improve diagnosis effectiveness of CSCD detection and diagnosis.

Inclusion Criteria:

• ① Men and women age 40 years or older;

  • At least one vascular risk factor has been identified, including hypertension, diabetes, hyperlipidemia, coronary heart disease, and chronic kidney disease；

    • The patient performed two brain MRI Examinations simultaneously at a time interval of more than 6 months (≥6).

Exclusion Criteria:

• ① The patient had no vascular risk factors；

  • No clinical follow-up images;

    • There are significant motion artifacts in the image, which cannot meet the

This is a multicenter study with a total plan of 1000 patients, 500 from Chinese PLA General Hospital and 500 from other centers.