Clinical trial NCT06667635: Intelligent Analysis and Clinical Validation of Cerebral Small Vessel Disease on Magnetic Resonance Imaging：A Multi-center Study
Official title: Intelligent Analysis and Clinical Validation of Cerebral Small Vessel Disease on Magnetic Resonance Imaging：A Multi-center Study
Study type: OBSERVATIONAL
Overall status: NOT_YET_RECRUITING
Conditions: Cerebral Small Vessel Disease
Keywords: Cerebral small vessel disease
Artificial intelligence
Deep learning
Magnetic resonance imaging
Lead sponsor: Chinese PLA General Hospital
Sponsor class: OTHER
First posted: 2024-10-31
Enrollment: 1000
Sex: ALL
Minimum age (years): 40
Standard ages: ADULT
OLDER_ADULT
Accepts healthy volunteers: yes
Observational model: COHORT
Time perspective: PROSPECTIVE
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.

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.

Eligibility criteria: 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

Primary outcomes:
measure: The performance of AI in lesion detection and diagnosis
timeFrame: 2 year
Secondary outcomes:
Other outcomes:
Locations:
city: Beijing
country: China
facility: Chinese PLA General Hospital
Study officials:
affiliation: Chinese PLA General Hospital
name: Xin Lou, MD/PhD
role: STUDY_CHAIR
References: