Predicting stroke risk in patients with chronic intracranial arterial occlusion using advanced imaging techniques

Is this trial a fit for you?

A plain-English reading of the study's rules. Only the study team can confirm whether you qualify.

What this trial studies

This project aims to develop a machine learning-based model to assess long-term stroke risk in asymptomatic patients with chronic intracranial arterial occlusion. It will utilize multimodal CT scans to create a deep learning segmentation model for key brain regions, including the basal ganglia and lobes of the brain. The study will involve training the model on retrospective data and validating it with prospective data to ensure accuracy in predicting both ischemic and hemorrhagic stroke risks. The research seeks to enhance the understanding of neuroimaging features related to stroke in this patient population.

Why it matters

Who can join: the full rules

Inclusion Criteria:

* Diagnosed with chronic intracranial arterial occlusion.
* Asymptomatic or with a history of only transient ischemic attacks (TIAs).
* The age of the patient falls within the range of 18 to 80 years old.
* Willing to undergo both perfusion CT scans and magnetic resonance imaging (MRI).

Exclusion Criteria:

* Acute intracranial arterial occlusive disease.
* History of either ischemic or hemorrhagic stroke.
* Allergies to contrast agents or conditions such as claustrophobia that prevent completion of imaging data acquisition.

Where it's running

• Beijing Tiantan Hospital
  Beijing, Beijing, China Recruiting
• Beijing Hospital
  Beijing, Beijing, China Recruiting

Study contacts

• Study coordinator: Shaosen Zhang, Doctor
• Email: longyoubafang@hotmail.com
• Phone: 18611284839

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How to take part

1. Review the eligibility criteria above with your treating physician.
2. Check the official listing on ClinicalTrials.gov for the latest contact details and recruitment status.
3. Contact the study coordinator or principal investigator to ask for pre-screening. Pre-screening is free and never obligates you to enroll.