Cerebral Small Vessel Disease
Conditions
Keywords
Cerebral small vessel disease, Artificial intelligence, Deep learning, Magnetic resonance imaging
Brief summary
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.
Detailed description
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.
Interventions
DIAGNOSTIC_TESTArtificial intelligence
Artificial intelligence (AI) tools developed through the training of large amounts of image data can assist with the analysis and interpretation of neuroimaging data of cerebral small vascular disease（CSVD）.
Sponsors
Chinese PLA General Hospital
Study design
Observational model
COHORT
Time perspective
PROSPECTIVE
Eligibility
Sex/Gender
ALL
Age
40 Years to No maximum
Healthy volunteers
Yes
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
Design outcomes
Primary
| Measure | Time frame | Description |
|---|---|---|
| The performance of AI in lesion detection and diagnosis | 2 year | The performance of AI in lesion detection and diagnosis, including imaging quality, accuracy, sensitivity and specificity in lesion detection and imaging diagnosis. |
Countries
China
Contacts
Primary ContactChaobang Xie
Outcome results
None listed