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Intelligent Analysis and Clinical Validation of Cerebral Small Vessel Disease on Magnetic Resonance Imaging：A Multi-center Study

NCT ID: NCT06667635

Last Updated: 2024-10-31

Study Results

Results pending

The study team has not published outcome measurements, participant flow, or safety data for this trial yet. Check back later for updates.

Basic Information

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Recruitment Status

NOT_YET_RECRUITING

Total Enrollment

1000 participants

Study Classification

OBSERVATIONAL

Study Start Date

2024-11-01

Study Completion Date

2030-09-01

Brief Summary

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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

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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.

Conditions

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Cerebral Small Vessel Disease

Keywords

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Cerebral small vessel disease Artificial intelligence Deep learning Magnetic resonance imaging

Study Design

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Observational Model Type

COHORT

Study Time Perspective

PROSPECTIVE

Interventions

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Artificial 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）.

Intervention Type DIAGNOSTIC_TEST

Eligibility Criteria

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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
Minimum Eligible Age

40 Years

Eligible Sex

ALL

Accepts Healthy Volunteers

Yes

Sponsors

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Chinese PLA General Hospital

OTHER

Sponsor Role lead

Responsible Party

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Xin Lou

Deputy Director of Department of Radiology

Responsibility Role PRINCIPAL_INVESTIGATOR

Principal Investigators

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Xin Lou, MD/PhD

Role: STUDY_CHAIR

Chinese PLA General Hospital

Locations

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Chinese PLA General Hospital

Beijing, China, China

Site Status

Countries

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China

Central Contacts

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Chaobang Xie

Role: CONTACT

Phone: 18798120676

Email: [email protected]

Facility Contacts

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Chaobang Xie

Role: primary

Other Identifiers

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2024-685

Identifier Type: -

Identifier Source: org_study_id