SVP Detection Using Machine Learning (SVP-ML)

March 5, 2024 updated by: King's College London

Automated Detection of Spontaneous Venous Pulsations Within Fundal Videos Using Machine Learning

This diagnostic study will use 410 retrospectively captured fundal videos to develop ML systems that detect SVPs and quantify ICP. The ground truth will be generated from the annotations of two independent, masked clinicians, with arbitration by an ophthalmology consultant in cases of disagreement.

Study Overview

Status

Active, not recruiting

Conditions

Intervention / Treatment

Study Type

Observational

Enrollment (Estimated)

210

Contacts and Locations

This section provides the contact details for those conducting the study, and information on where this study is being conducted.

Study Locations

Participation Criteria

Researchers look for people who fit a certain description, called eligibility criteria. Some examples of these criteria are a person's general health condition or prior treatments.

Eligibility Criteria

Ages Eligible for Study

18 years and older (Adult, Older Adult)

Accepts Healthy Volunteers

Yes

Sampling Method

Non-Probability Sample

Study Population

Patients aged ≥18 years with presumed normal ICP or suspected raised ICP

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

This section provides details of the study plan, including how the study is designed and what the study is measuring.

How is the study designed?

Design Details

Cohorts and Interventions

Group / Cohort
Intervention / Treatment
Patients aged ≥18 years with presumed normal intracranial pressure
Diagnostic test: Machine Learning Model
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
Diagnostic test: Machine Learning Model
Automated machine learning system for the detection of spontaneous venous pulsations and quantification of intracranial pressure

What is the study measuring?

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

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

This is where you will find people and organizations involved with this study.

Sponsor

King's College London

Study record dates

These dates track the progress of study record and summary results submissions to ClinicalTrials.gov. Study records and reported results are reviewed by the National Library of Medicine (NLM) to make sure they meet specific quality control standards before being posted on the public website.

Study Major Dates

Study Start (Actual)

March 1, 2023

Primary Completion (Estimated)

November 1, 2024

Study Completion (Estimated)

November 1, 2024

Study Registration Dates

First Submitted

January 30, 2023

First Submitted That Met QC Criteria

February 7, 2023

First Posted (Actual)

February 16, 2023

Study Record Updates

Last Update Posted (Estimated)

March 6, 2024

Last Update Submitted That Met QC Criteria

March 5, 2024

Last Verified

March 1, 2024

More Information

Terms related to this study

Additional Relevant MeSH Terms

Other Study ID Numbers

  • 1.0

Plan for Individual participant data (IPD)

Plan to Share Individual Participant Data (IPD)?

YES

IPD Plan Description

The data would be shared, where possible, through a restricted-access data sharing agreement, where in line with KCL data governance requirements.

IPD Sharing Time Frame

Within 12 months of study completion

IPD Sharing Access Criteria

Data sharing agreement and data governance

IPD Sharing Supporting Information Type

  • STUDY_PROTOCOL

Drug and device information, study documents

Studies a U.S. FDA-regulated drug product

No

Studies a U.S. FDA-regulated device product

No

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.

Clinical Trials on Intracranial Pressure Increase

Clinical Trials on Machine Learning Model

Search Similar Trials

Sponsors and Collaborators

Medical Conditions

Drug Interventions

CROs by country

CROs in Venezuela

Conditions

Rare Diseases

Drug Interventions

Dietary Supplements

Sponsor/Collaborators

Locations

Subscribe