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

NCT ID: NCT06978348

Last Updated: 2025-05-18

Basic Information

• Recruitment Status: RECRUITING
• Total Enrollment: 244296 participants
• Study Classification: OBSERVATIONAL
• Study Start Date: 2025-05-10
• Study Completion Date: 2025-06-10

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.

Conditions

Ischemic Cerebrovascular Disease; Artificial Intelligence; Prediction Model

Study Design

• Observational Model Type: CASE_ONLY
• Study Time Perspective: RETROSPECTIVE

Eligibility Criteria

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

• Eligible Sex: ALL
• Accepts Healthy Volunteers: No

Sponsors

Shanghai Jiao Tong University School of Medicine

OTHER

Sponsor Role: lead

Responsible Party

Yijun Cheng

Principal investigator

Responsibility Role: PRINCIPAL_INVESTIGATOR

Principal Investigators

Yijun Cheng, Doc

Role: PRINCIPAL_INVESTIGATOR

Ruijin Hospital

Hanbing Shang, Doc

Role: PRINCIPAL_INVESTIGATOR

Ruijin Hospital

Locations

Model

Shanghai, Shanghai Municipality, China

Site Status: RECRUITING

Countries

China

Central Contacts

Yijun Cheng, Doc

Role: CONTACT

cyj12574@rjh.com.cn

86+15021058538

Facility Contacts

Yijun Cheng

Role: primary

Other Identifiers

AI-iSTROKE

Identifier Type: -

Identifier Source: org_study_id