Automatic Diagnosis of Spinal Stenosis on CT (ASSIST)
Automatic Diagnosis of Spinal Stenosis on CT With Deep Learning
Study Overview
Status
Status
Conditions
Conditions
Intervention / Treatment
Intervention / Treatment
Detailed Description
Study Type
Study Type
Enrollment (Anticipated)
Enrollment
Contacts and Locations
Study Contact
Study Contact
- Name: Shisheng He, MD
- Phone Number: 021-66307580
- Email: TJHSS7418@TONGJI.EDU.CN
Study Contact Backup
- Name: GUOXIN FAN, MD
- Phone Number: 021-66307580
- Email: GFAN@TONGJI.EDU.CN
Participation Criteria
Eligibility Criteria
Eligibility Criteria
Ages Eligible for Study
Accepts Healthy Volunteers
Genders Eligible for Study
Sampling Method
Study Population
Spinal stenosis is a narrowing of the spaces within the spine, which can put pressure on the nerves that travel through the spine. Spinal stenosis occurs most often in the back, the neck, and sometimes the thoracic spine.
Some people with spinal stenosis may not have symptoms. Others may experience pain, tingling, numbness and muscle weakness. Symptoms can worsen over time.
Description
Inclusion Criteria:
- Age >18 years
- with radiologists' CT reports on cervical, thoracic and lumbar stenosis
Exclusion Criteria:
- not applicable (only specific levels with extensive infections, fractures, tumor, high-grade spondylolisthesis would be excluded for analysis).
Study Plan
How is the study designed?
Design Details
- Observational Models: Case-Only
- Time Perspectives: Retrospective
Number of groups / cohorts
Cohorts and Interventions
Group / CohortGroup / Cohort |
Intervention / TreatmentIntervention / Treatment |
|---|---|
|
spinal stenosis
Spinal stenosis is a narrowing of the spaces within your spine, which can put pressure on the nerves that travel through the spine.
Spinal stenosis occurs most often in the lower back and the neck.
|
detect and classify spinal stenosis by deep learning
|
What is the study measuring?
Primary Outcome Measures
Primary Outcome Measures
Outcome Measure |
Measure Description |
Time Frame |
|---|---|---|
|
diagnostic accuracy of deep learning
Time Frame: 1 day
|
Diagnostic accuracy of deep learning to determine spinal stenosis compared with radiologists' labels based on CT
|
1 day
|
Secondary Outcome Measures
Secondary Outcome Measures
Outcome Measure |
Measure Description |
Time Frame |
|---|---|---|
|
Diagnostic Performance of deep learning
Time Frame: 1 day
|
Sensitivity, specificity, positive predictive value and negative predictive value of deep learning compared with radiologists' labels based on CT
|
1 day
|
Collaborators and Investigators
Sponsor
Sponsor
Collaborators
Collaborators
Study record dates
Study Major Dates
Study Start (Anticipated)
Study Start
Primary Completion (Anticipated)
Primary Completion
Study Completion (Anticipated)
Study Completion
Study Registration Dates
First Submitted
First Submitted
First Submitted That Met QC Criteria
First Submitted That Met QC Criteria
First Posted (Actual)
First Posted
Study Record Updates
Last Update Posted (Actual)
Last Update Posted
Last Update Submitted That Met QC Criteria
Last Update Submitted That Met QC Criteria
Last Verified
Last Verified
More Information
Terms related to this study
Additional Relevant MeSH Terms
Other Study ID Numbers
Other Study ID Numbers
- SHSY181022
Plan for Individual participant data (IPD)
Plan to Share Individual Participant Data (IPD)?
Drug and device information, study documents
Studies a U.S. FDA-regulated drug product
Studies a U.S. FDA-regulated device product
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