Research on Early Prediction Model of Ischemic Cerebrovascular Disease Based on Artificial Intelligence Technology.

Recruiting

• Conditions: Ischemic Cerebrovascular Disease; Artificial Intelligence; Prediction Model

• Registration Number: NCT06978348
• Lead Sponsor: Shanghai Jiao Tong University School of Medicine

Brief Summary

Establish an artificial intelligence clinical decision support system for patients with carotid/vertebral artery cerebrovascular stenosis, early identification of patients who may have cerebral infarction. With the support of this project, it is expected that a secondary prevention clinical decision support system for chronic stroke will be established, which is likely to become an important auxiliary tool for the management of cerebrovascular diseases in the future.

Detailed Description

Stroke is a disease with a high mortality rate and incidence rate, and it is one of the main reasons for high medical expenses. Ischemic stroke accounts for approximately 85% of all subtypes of stroke. Carotid artery and vertebral artery stenosis are definite and intervenable risk factors for ischemic stroke. However, the selection of clinical intervention timing and methods for patients with cerebrovascular stenosis is limited to the rate of carotid/vertebral artery stenosis and the symptoms of the patients. Cerebral infarction caused by carotid/vertebral artery stenosis often leads to irreparable neurological deficits. Currently, there is a lack of comprehensive evaluation methods for the severity of ischemic cerebrovascular diseases such as carotid/vertebral artery stenosis, and even less a clinical decision-making system that can predict the progression of the disease. This project intends to take the demographic data and clinical information of patients with cerebrovascular stenosis from multiple centers and ethnic groups as the entry point, combine the multidisciplinary advantages of imaging, ultrasound, clinical medicine and computer science, and use artificial intelligence technology to construct a model for predicting the disease progression and the probability of adverse cardiovascular events such as stroke in patients with cerebrovascular stenosis. Based on this, the investigators' hospital intends to develop a set of secondary prevention management tools and clinical decision support systems for ischemic cerebrovascular diseases.

Study Timeline

• Study First Submitted: 2025-04-18
• Status Verified: 2025-05
• Study Start: 2025-05-10
• Study First Submitted QC: 2025-05-10
• Last Update Submitted: 2025-05-10
• Study First Posted: 2025-05-18
• Last Update Posted: 2025-05-18
• Primary Completion: 2025-06-10
• Study Completion: 2025-06-10

Recruitment & Eligibility

• Status: RECRUITING
• Sex: All
• Target Recruitment: 244296

Inclusion Criteria

Patients undergoing vascular (carotid/vertebral artery) B-ultrasound

Exclusion Criteria

Patients with missing clinical data such as medical history, cerebrovascular ultrasound results and biochemical data

Study & Design

• Study Type: OBSERVATIONAL
• Study Design: Not specified

Primary Outcome Measures

Name	Time	Method
Establish an artificial intelligence clinical decision support system for patients with carotid/vertebral artery cerebrovascular stenosis，Early identification of patients who may have cerebral infarction.	December 2025	1.The clinical history, imaging data, blood test indicators and other data of patients who completed carotid/vertebral artery cerebrovascular ultrasound examinations from January 2012 to December 2022 were collected to establish a data set；2. This dataset was statistically analyzed in combination with the general risk factors of cerebrovascular diseases and the specific risk factors of carotid artery stenosis；3. The above-mentioned model was trained using the existing clinical database of patients with carotid and cerebrovascular stenosis in the hospital；4. Through machine learning, an artificial intelligence clinical decision support system for patients with carotid/vertebral artery stenosis is established to identify patients with early cerebrovascular stenosis who require surgical intervention, and even asymptomatic patients.

Secondary Outcome Measures

Name	Time	Method
Analyze the risk factors leading to stroke	December 2025	By collecting the baseline demographic data, clinical history, imaging, laboratory tests and other data of all patients who underwent carotid/vertebral artery vascular ultrasound during the period from 2012.01 to 2022.12, the risk factors causing stroke were analyzed.

Trial Locations

Locations (1)

Model; 🇨🇳 Shanghai, Shanghai, China