Deep Enhanced Imaging in Stroke and Vascular Neurology

February 7, 2023 updated by: Xin Lou, Chinese PLA General Hospital
To investigate the performance of enhanced computed tomography (CT) or magnetic resonance (MR) imaging by deep learning relative to conventional CT or MR imaging in brain stroke and vascular neurology. We expect that the deep enhanced imaging method can shorten the time stay in the imaging session of stroke patients, optimize the overall imaging quality and improve the patients' care in imaging session.

Study Overview

Status

Recruiting

Conditions

Intervention / Treatment

Detailed Description

Early diagnosis of cerebral infarction, detection of ischemic penumbra, evaluation of collateral circulation and identification of vascular lesions by imaging are critical for treatment decision and outcome improvement in cerebral stroke. Multimodal computed tomography (CT) and magnetic resonance (MR) imaging are most prevalent and accessible approaches in clinical scenarios. These two approaches are downgraded either by radiation exposure or long scanning time which may hinder the rapid treatment for patients. Deep learning has shown substantial achievements in medical imaging enhancement. The added value of deep learning method in stroke and vascular neurology has not been thoroughly validated. In this study, we aimed to investigate the performance of enhanced computed tomography (CT) or magnetic resonance (MR) imaging by deep learning relative to conventional CT or MR imaging in brain stroke and vascular neurology. We expect that the deep enhanced imaging method can shorten the time stay in the imaging session of stroke patients, optimize the overall imaging quality and improve the patients' care in imaging session.

Study Type

Observational

Enrollment (Anticipated)

1000

Contacts and Locations

This section provides the contact details for those conducting the study, and information on where this study is being conducted.

Study Locations

  • China
    • Beijing
      • Beijing, Beijing, China, 100853
        • Recruiting
        • Chinese PLA General Hospital

Participation Criteria

Researchers look for people who fit a certain description, called eligibility criteria. Some examples of these criteria are a person's general health condition or prior treatments.

Eligibility Criteria

Ages Eligible for Study

18 years to 100 years (ADULT, OLDER_ADULT)

Accepts Healthy Volunteers

No

Genders Eligible for Study

All

Sampling Method

Non-Probability Sample

Study Population

Patients have experienced stroke or cerebral ischemia and undergone brain imaging and vascular imaging.

Description

Inclusion Criteria:

  • suspecting to have experienced stroke or cerebral ischemia and needed to undergo brain imaging and vascular imaging including CT or MRI
  • no history of kidney failure
  • a minimum age of 18 years
  • obtained written informed consent

Exclusion Criteria:

  • severe movement artifacts
  • incidental finding of tumor lesion or craniocerebral surgery history
  • poor imaging failed to perform deep learning method
  • women who pregnancy

Study Plan

This section provides details of the study plan, including how the study is designed and what the study is measuring.

How is the study designed?

Design Details

  • Observational Models: Case-Only
  • Time Perspectives: Prospective

Cohorts and Interventions

Group / Cohort
Intervention / Treatment
Imaging group
Participants with suspecting brain stroke or vascular lesion conducted conventional CT or MR imaging and deep enhanced imaging.
Diagnostic test: Deep learning imaging enhancement
Conventional imaging or down-sampling imaging from CT or MR are enhanced by approved deep learning method.

What is the study measuring?

Primary Outcome Measures

Outcome Measure
Measure Description
Time Frame
The performance of deep enhanced imaging in lesion detection and diagnosis
Time Frame: 1 year
The performance of deep enhanced imaging in lesion detection and diagnosis, including imaging quality, accuracy, sensitivity and specificity in lesion detection and imaging diagnosis.
1 year

Collaborators and Investigators

This is where you will find people and organizations involved with this study.

Sponsor

Chinese PLA General Hospital

Study record dates

These dates track the progress of study record and summary results submissions to ClinicalTrials.gov. Study records and reported results are reviewed by the National Library of Medicine (NLM) to make sure they meet specific quality control standards before being posted on the public website.

Study Major Dates

Study Start (ACTUAL)

December 1, 2022

Primary Completion (ANTICIPATED)

December 31, 2027

Study Completion (ANTICIPATED)

December 31, 2027

Study Registration Dates

First Submitted

November 5, 2022

First Submitted That Met QC Criteria

November 5, 2022

First Posted (ACTUAL)

November 14, 2022

Study Record Updates

Last Update Posted (ACTUAL)

February 8, 2023

Last Update Submitted That Met QC Criteria

February 7, 2023

Last Verified

February 1, 2023

More Information

Terms related to this study

Keywords

Additional Relevant MeSH Terms

Other Study ID Numbers

  • AI-301

Drug and device information, study documents

Studies a U.S. FDA-regulated drug product

No

Studies a U.S. FDA-regulated device product

No

This information was retrieved directly from the website clinicaltrials.gov without any changes. If you have any requests to change, remove or update your study details, please contact register@clinicaltrials.gov. As soon as a change is implemented on clinicaltrials.gov, this will be updated automatically on our website as well.

Clinical Trials on Vascular Diseases

Clinical Trials on Deep learning imaging enhancement

Search Similar Trials

Sponsors and Collaborators

Medical Conditions

Drug Interventions

CROs by country

CROs in Belarus

Conditions

Rare Diseases

Drug Interventions

Dietary Supplements

Sponsor/Collaborators

Locations

Subscribe