SVP Detection Using Machine Learning (SVP-ML)
Automated Detection of Spontaneous Venous Pulsations Within Fundal Videos Using Machine Learning
Study Overview
Status
Status
Conditions
Conditions
Intervention / Treatment
Intervention / Treatment
Study Type
Study Type
Enrollment (Estimated)
Enrollment
Contacts and Locations
Study Contact
Study Contact
- Name: Tim Jackson, PhD
- Phone Number: +44 02032991297
- Email: tim.jackson@kcl.ac.uk
Study Locations
-
-
-
London, United Kingdom
- King's College London
-
-
Participation Criteria
Eligibility Criteria
Eligibility Criteria
Ages Eligible for Study
Accepts Healthy Volunteers
Sampling Method
Study Population
Description
Inclusion Criteria:
- Patients aged ≥18 years with presumed normal ICP undergoing routine dilated OCT scans.
- Patients undergoing a LP or continuous ICP monitoring with implanted transcranial pressure transducer devices at in- or out-patient neurology, neurosurgery or neuro-ophthalmology services.
Exclusion Criteria:
- Glaucoma diagnosis or glaucoma suspects in either eye.
- Bilateral restricted fundal view, e.g. advanced bilateral cataracts.
- Bilateral retinal vein or artery occlusion.
Study Plan
How is the study designed?
Design Details
Number of groups / cohorts
Cohorts and Interventions
Group / CohortGroup / Cohort |
Intervention / TreatmentIntervention / Treatment |
|---|---|
|
Patients aged ≥18 years with presumed normal intracranial pressure
|
Automated machine learning system for the detection of spontaneous venous pulsations and quantification of intracranial pressure
|
|
Patients aged ≥18 years with suspected raised intracranial pressure
|
Automated machine learning system for the detection of spontaneous venous pulsations and quantification of intracranial pressure
|
What is the study measuring?
Primary Outcome Measures
Primary Outcome Measures
Outcome Measure |
Measure Description |
Time Frame |
|---|---|---|
|
Area-under-the receiver operating characteristic (AUROC) for spontaneous venous pulsations detection
Time Frame: 1 year
|
Binary classification performance of the machine learning model
|
1 year
|
Secondary Outcome Measures
Secondary Outcome Measures
Outcome Measure |
Measure Description |
Time Frame |
|---|---|---|
|
Localisation of spontaneous venous pulsations
Time Frame: 1 year
|
Bounding box overlap for the machine learning model
|
1 year
|
|
Quantification of intracranial pressure
Time Frame: 1 year
|
Mean absolute error for the prediction of the intracranial pressure
|
1 year
|
Collaborators and Investigators
Sponsor
Sponsor
Study record dates
Study Major Dates
Study Start (Actual)
Study Start
Primary Completion (Estimated)
Primary Completion
Study Completion (Estimated)
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 (Estimated)
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
- 1.0
Plan for Individual participant data (IPD)
Plan to Share Individual Participant Data (IPD)?
IPD Plan Description
IPD Sharing Time Frame
IPD Sharing Access Criteria
IPD Sharing Supporting Information Type
- STUDY_PROTOCOL
Drug and device information, study documents
Studies a U.S. FDA-regulated drug product
Studies a U.S. FDA-regulated device product
This information was retrieved directly from the website clinicaltrials.gov without any changes. If you have any requests to change, remove or update your study details, please contact register@clinicaltrials.gov. As soon as a change is implemented on clinicaltrials.gov, this will be updated automatically on our website as well.