Clinical trial NCT05614193: Deep Enhanced Imaging in Stroke and Vascular Neurology
Official title: Deep Enhanced Imaging in Stroke and Vascular Neurology
Study type: OBSERVATIONAL
Overall status: RECRUITING
Conditions: Radiology
Cerebral Stroke
Vascular Diseases
Keywords: Deep learning
Medical imaging
Cerebral stroke
Cerebrovascular disease
Vascular imaging
Lead sponsor: Chinese PLA General Hospital
Sponsor class: OTHER
First posted: 2022-11-14
Enrollment: 1000
Sex: ALL
Minimum age (years): 18
Maximum age (years): 100
Standard ages: ADULT
OLDER_ADULT
Accepts healthy volunteers: no
Observational model: CASE_ONLY
Time perspective: PROSPECTIVE
Sampling method: NON_PROBABILITY_SAMPLE
Study population: Patients have experienced stroke or cerebral ischemia and undergone brain imaging and vascular imaging.

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.

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

Primary outcomes:
measure: The performance of deep enhanced imaging in lesion detection and diagnosis
timeFrame: 1 year
Secondary outcomes:
Other outcomes:
Locations:
city: Beijing
country: China
facility: Chinese PLA General Hospital
state: Beijing
Study officials:
affiliation: Chinese PLA General Hospital
name: Lou Xin
role: STUDY_CHAIR
References: