Ignite Creation Date:
2025-12-24 @ 11:58 AM
Ignite Modification Date:
2026-02-25 @ 9:46 PM
Study NCT ID:
NCT03746561
Status:
UNKNOWN
Last Update Posted:
2018-11-19
First Post:
2018-11-07
Is Gene Therapy:
True
Has Adverse Events:
False
Brief Title:
Automatic Diagnosis of Spinal Stenosis on CT
Sponsor:
Shanghai 10th People's Hospital
Organization:
Study Overview
Official Title:
Automatic Diagnosis of Spinal Stenosis on CT With Deep Learning
Status:
UNKNOWN
Status Verified Date:
2018-11
Last Known Status:
NOT_YET_RECRUITING
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:
ASSIST
Brief Summary:
MRI is a common tool for radiographic diagnosis of spinal stenosis, but it is expensive and requires long scanning time. CT is also a useful tool to diagnose spinal stenosis, yet interpretation can be time-consuming with high inter-reader variability even among the most specialized radiologists. In this study, the investigators aim to develop a deep-learning algorithm to automatically detect and classify lumbar spinal stenosis.
Detailed Description:
MRI is a common tool for radiographic diagnosis of spinal stenosis, but it is expensive and requires long scanning time. CT is also a useful tool to diagnose spinal stenosis, yet interpretation can be time-consuming with high inter-reader variability even among the most specialized radiologists. In this study, the investigators aim to develop a deep-learning algorithm to automatically detect and classify lumbar spinal stenosis. It would be a time-saving workflow if the software can assist the radiologists to detect and locate the suspected lesion.
Study Oversight
Has Oversight DMC:
True
Is a FDA Regulated Drug?:
False
Is a FDA Regulated Device?:
False
Is an Unapproved Device?:
None
Is a PPSD?:
None
Is a US Export?:
None
Is an FDA AA801 Violation?: