Clinical trial NCT06978348: Research on Early Prediction Model of Ischemic Cerebrovascular Disease Based on Artificial Intelligence Technology.
Official title: Establish an Artificial Intelligence Clinical Decision Support System for Patients With Carotid/Vertebral Artery Stenosis.
Study type: OBSERVATIONAL
Overall status: RECRUITING
Conditions: Ischemic Cerebrovascular Disease
Artificial Intelligence
Prediction Model
Lead sponsor: Shanghai Jiao Tong University School of Medicine
Sponsor class: OTHER
First posted: 2025-05-18
Enrollment: 244296
Sex: ALL
Standard ages: CHILD
ADULT
OLDER_ADULT
Accepts healthy volunteers: no
Observational model: CASE_ONLY
Time perspective: RETROSPECTIVE
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.

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.

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

Primary outcomes:
measure: 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.
timeFrame: December 2025
Secondary outcomes:
measure: Analyze the risk factors leading to stroke
timeFrame: December 2025
Other outcomes:
Locations:
city: Shanghai
country: China
facility: Model
state: Shanghai
Study officials:
affiliation: Ruijin Hospital
name: Yijun Cheng, Doc
role: PRINCIPAL_INVESTIGATOR
affiliation: Ruijin Hospital
name: Hanbing Shang, Doc
role: PRINCIPAL_INVESTIGATOR
References: