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

NCT06667635 | Not Yet Recruiting

• 1 other identifier: 2024-685
• Study Type: observational
• Target: 1,000
• Locations: 1 country
• Sites: 1

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.

Conditions

Cerebral Small Vessel Disease

Keywords

Cerebral small vessel disease; Artificial intelligence; Deep learning; Magnetic resonance imaging

Outcome Measures

Primary Outcomes (1)

• The performance of AI in lesion detection and diagnosis

  The performance of AI in lesion detection and diagnosis, including imaging quality, accuracy, sensitivity and specificity in lesion detection and imaging diagnosis.

  2 year

Interventions

Artificial intelligence DIAGNOSTIC_TEST

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）.

Eligibility Criteria

• Age: 40 Years+
• Sex: all
• Healthy Volunteers: Yes
• Age Groups: Adult (18-64), Older Adult (65+)
• Sampling Method: Non-Probability Sample

Study Population

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

You may qualify if:

• 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).

You may not qualify if:

• 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

Sponsors & Collaborators

• Chinese PLA General Hospital: lead

Study Sites (1)

Chinese PLA General Hospital

Beijing, China, 100853, China

Location

MeSH Terms

Conditions

Cerebral Small Vessel Diseases

Interventions

Artificial Intelligence

Condition Hierarchy (Ancestors)

Cerebrovascular Disorders; Brain Diseases; Central Nervous System Diseases; Nervous System Diseases; Vascular Diseases; Cardiovascular Diseases

Intervention Hierarchy (Ancestors)

Algorithms; Mathematical Concepts

Study Officials

• Xin Lou, MD/PhD

  Chinese PLA General Hospital

  STUDY CHAIR

Central Study Contacts

Chaobang Xie

CONTACT

18798120676; chaobangxie@163.com

Study Design

• Study Type: observational
• Observational Model: COHORT
• Time Perspective: PROSPECTIVE
• Sponsor Type: OTHER
• Responsible Party: PRINCIPAL INVESTIGATOR
• PI Title: Deputy Director of Department of Radiology

Study Record Dates

• First Submitted: October 22, 2024
• First Posted: October 31, 2024
• Study Start: November 1, 2024
• Primary Completion (Estimated): September 1, 2030
• Study Completion (Estimated): September 1, 2030
• Last Updated: October 31, 2024

Record last verified: 2024-09

Locations

CN (1)