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

NCT06978348 | Recruiting

• 1 other identifier: AI-iSTROKE
• Study Type: observational
• Target: 244,296
• Locations: 1 country
• Sites: 1

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.

Conditions

Ischemic Cerebrovascular Disease; Artificial Intelligence; Prediction Model

Outcome Measures

Primary Outcomes (1)

• 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.

  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.

  December 2025

Secondary Outcomes (1)

• Analyze the risk factors leading to stroke

  December 2025

Eligibility Criteria

• Sex: all
• Healthy Volunteers: No
• Age Groups: Child (0-17), Adult (18-64), Older Adult (65+)
• Sampling Method: Non-Probability Sample

Study Population

1. Patients who visited our hospital from January, 2012 to January, 2022; 2. Patients undergoing cervical/vertebral artery cerebrovascular ultrasonography those.

You may qualify if:

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

You may not qualify if:

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

Sponsors & Collaborators

• Shanghai Jiao Tong University School of Medicine: lead

Study Sites (1)

Model

Shanghai, Shanghai Municipality, China

RECRUITING

Study Officials

• Yijun Cheng, Doc

  Ruijin Hospital

  PRINCIPAL INVESTIGATOR

• Hanbing Shang, Doc

  Ruijin Hospital

  PRINCIPAL INVESTIGATOR

Central Study Contacts

Yijun Cheng, Doc

CONTACT

86+15021058538; cyj12574@rjh.com.cn

Study Design

• Study Type: observational
• Observational Model: CASE ONLY
• Time Perspective: RETROSPECTIVE
• Sponsor Type: OTHER
• Responsible Party: PRINCIPAL INVESTIGATOR
• PI Title: Principal investigator

Study Record Dates

• First Submitted: April 18, 2025
• First Posted: May 18, 2025
• Study Start: May 10, 2025
• Primary Completion: June 10, 2025
• Study Completion: June 10, 2025
• Last Updated: May 18, 2025

Record last verified: 2025-05

Data Sharing

• IPD Sharing: Will not share

Locations

CN (1)