Deep Enhanced Imaging in Stroke and Vascular Neurology | NCT05614193

Studies
Deep Enhanced Imaging in Stroke and Vascular Neurology

NCT05614193

Sponsor

Chinese PLA General Hospital

Status

Recruiting

Last Update Posted

Feb 8, 2023Actual

Enrollment

1,000Estimated

Phase

Not provided

Conditions

Radiology
Cerebral Stroke
Vascular Diseases

Interventions

Deep learning imaging enhancement

Countries

China

Protocol Section

Identification Module Status Module Sponsor/Collaborators Module Oversight Module Description Module Conditions Module Design Module Arms/Interventions Module Outcomes Module Eligibility Module Contacts/Locations Module References Module IPD Sharing Statement Module

Results Section

Participant Flow Module Baseline Characteristics Module Outcome Measures Module Adverse Events Module More Info Module

Annotation Section

Annotation Module

Document Section

Large Document Module

Derived Section

Miscellaneous Info Module Condition Browse Module Intervention Browse Module

Protocol Section

Identification Module

NCT ID

Results Section

No data available

No data is available for this block.

Annotation Section

No data available

No data is available for this block.

Document Section

No data available

No data is available for this block.

Derived Section

Miscellaneous Info Module

Version Holder

Status Module

Record Verification Date

Feb 2023

Overall Recruitment Status or Expanded Access Status

Recruiting

Last Known Status

Not provided

Delayed Posting

Not provided

Why Stopped

Not provided

Expanded Access Info

Start Date

Dec 1, 2022Actual

Primary Completion Date

Dec 31, 2027Estimated

Completion Date

Dec 31, 2027Estimated

First Submitted Date

Nov 5, 2022

First Submission Date that Met QC Criteria

Nov 5, 2022

First Posted Date

Nov 14, 2022Actual

Results Waived

Not provided

Results First Submitted Date

Not provided

Results First Submitted that Met QC Criteria

Not provided

Results First Posted Date

Not provided

Certification/Extension (aka Delayed Results) First Submitted Date

Not provided

Certification/Extension First Submitted that Passed QC Review

Not provided

Certification/Extension First Posted Date

Not provided

Last Update Submitted Date

Feb 7, 2023

Last Update Posted Date

Feb 8, 2023Actual

Sponsor/Collaborators Module

Responsible Party, by Official Title

Xin Lou, Chairman, Chinese PLA General HospitalPrincipal Investigator

Lead Sponsor

Chinese PLA General HospitalOTHER

Collaborators

Not provided

Oversight Module

Has Data Monitoring Committee (DMC)

Not provided

Is FDA Regulated Drug

Is FDA Regulated Device

Is Unapproved Device

Not provided

Pediatric Postmarket Surveillance of a Device Product

Not provided

Product Exported from US

Not provided

FDAAA801 Violation

Not provided

Description Module

Brief Summary

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.

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.

Design Module

Study Type

Observational

Number of References to an Expanded Access Study

Not provided

Expanded Access Types

Not provided

Patient Registry

Target Follow-Up Duration

Not provided

Phases

Not provided

Interventional Study Design

Allocation

Not provided

Intervention Model

Biospecimen

No data available

No data is available for this block.

Enrollment

1,000Estimated

Arms/Interventions Module

Arm Groups

Label	Type	Description	Intervention Names
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

Interventions

Name	Type	Description	Arm Group Labels	Other Names
Deep learning imaging enhancement	Diagnostic Test	Conventional imaging or down-sampling imaging from CT or MR are enhanced by approved deep learning method.	Imaging group

Outcomes Module

Primary Outcomes

Measure	Description	Time Frame
The performance of deep enhanced imaging in lesion detection and diagnosis	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

Secondary Outcomes

Not provided

Other Outcomes

Not provided

Eligibility Module

Eligibility Criteria

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

Accepts Healthy Volunteers

Sex

All

Sex/Gender Based

Not provided

Sex/Gender Description

Not provided

Minimum Age

18 Years

Maximum Age

100 Years

Standard Ages

AdultOlder Adult

Study Population

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

Sampling Method

Non-Probability Sample

Contacts/Locations Module

Central Contacts

Name	Role	Phone	Extension	Email
Jinhao Lyu	Contact	+8615903562929		330322990@qq.com

Overall Officials

Name	Affiliation	Role
Lou Xin	Chinese PLA General Hospital	Study Chair

Locations

Facility	Status	City	State	ZIP	Country	Contacts
Chinese PLA General Hospital	Recruiting	Beijing	Beijing Municipality	100853	China

Condition Browse Module

MeSH Terms

ID	Term
D020521	Stroke
D014652	Vascular Diseases
D002561	Cerebrovascular Disorders

Ancestor Terms

ID	Term
D001927	Brain Diseases
D002493	Central Nervous System Diseases
D009422	Nervous System Diseases
D002318	Cardiovascular Diseases

Browse Leaves

Not provided

Browse Branches

Not provided