Ignite Creation Date:
2024-05-06 @ 6:55 PM
Last Modification Date:
2024-10-26 @ 2:57 PM
Study NCT ID:
NCT05838456
Status:
COMPLETED
Last Update Posted:
2023-06-28
First Post:
2023-04-17
Brief Title:
Deep Learning Enabled Endovascular Stroke Therapy Screening in Community Hospitals
Sponsor:
The University of Texas Health Science Center Houston
Organization:
The University of Texas Health Science Center Houston
Study Overview
Official Title:
Deep Learning Enabled Endovascular Stroke Therapy Screening in Community Hospitals
Status:
COMPLETED
Status Verified Date:
2023-06
Last Known Status:
None
Delayed Posting:
No
If Stopped, Why?:
Not Stopped
Has Expanded Access:
False
If Expanded Access, NCT#:
N/A
Has Expanded Access, NCT# Status:
N/A
Acronym:
None
Brief Summary:
After onset of Acute Ischemic Stroke AIS every minute of delay to treatment reduces the likelihood of a good clinical outcome A key delay occurs in the time between completion of computed tomography CT angiography of the head and neck and interpretation in the setting of AIS care
The purpose of this study is to assess the effect of incorporating VizAI software which via via a machine-learning algorithm performs artificial intelligence-based automated detection of large vessel occlusions LVO on CT angiography CTA images and alerts the AIS care team diagnosis and treatment decisions will be based on the clinical evaluation and review of the images by the treating physician per routine standard of care The hypothesis is that integration of the software into the AIS care pathway will reduce delays in treatment A cluster-randomized stepped-wedge trial will be performed across 4 hospitals in the greater Houston area
The purpose of this study is to assess the effect of incorporating VizAI software which via via a machine-learning algorithm performs artificial intelligence-based automated detection of large vessel occlusions LVO on CT angiography CTA images and alerts the AIS care team diagnosis and treatment decisions will be based on the clinical evaluation and review of the images by the treating physician per routine standard of care The hypothesis is that integration of the software into the AIS care pathway will reduce delays in treatment A cluster-randomized stepped-wedge trial will be performed across 4 hospitals in the greater Houston area
Detailed Description:
None
Study Oversight
Has Oversight DMC:
None
Is a FDA Regulated Drug?:
False
Is a FDA Regulated Device?:
True
Is an Unapproved Device?:
None
Is a PPSD?:
None
Is a US Export?:
False
Is an FDA AA801 Violation?:
None
Secondary IDs
| Secondary ID | Type | Domain | Link |
|---|---|---|---|
| UL1TR003167 | NIH | None | httpsreporternihgovquickSearchUL1TR003167 |