SVP-ML: SVP Detection Using Machine Learning

Sponsor

King's College London (Other)

Overall Status

Not yet recruiting

CT.gov ID

NCT05731765

Collaborator

(none)

210

Enrollment

Anticipated Duration (Months)

Study Details

Study Description

Brief Summary

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.

Condition or Disease	Intervention/Treatment	Phase
Intracranial Pressure Increase	Diagnostic Test: Machine Learning Model

Study Design

Study Type:

Observational

Anticipated Enrollment :

210 participants

Observational Model:

Cohort

Time Perspective:

Retrospective

Official Title:

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

Anticipated Study Start Date :

Feb 1, 2023

Anticipated Primary Completion Date :

Oct 1, 2023

Anticipated Study Completion Date :

Oct 1, 2023

Arms and Interventions

Arm	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

Arm

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

Outcome Measures

Primary Outcome Measures

Area-under-the receiver operating characteristic (AUROC) for spontaneous venous pulsations detection [1 year]
Binary classification performance of the machine learning model

Secondary Outcome Measures

Localisation of spontaneous venous pulsations [1 year]
Bounding box overlap for the machine learning model
Quantification of intracranial pressure [1 year]
Mean absolute error for the prediction of the intracranial pressure

Eligibility Criteria

Criteria

Ages Eligible for Study:

18 Years and Older

Sexes Eligible for Study:

All

Accepts Healthy Volunteers:

Yes

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.

Contacts and Locations

Locations

No locations specified.

Sponsors and Collaborators

King's College London

Investigators

None specified.

Study Documents (Full-Text)

None provided.

More Information

Publications

None provided.

Responsible Party:

King's College London

ClinicalTrials.gov Identifier:

NCT05731765

Other Study ID Numbers:

First Posted:

Feb 16, 2023

Last Update Posted:

Feb 16, 2023

Last Verified:

Jan 1, 2023

Individual Participant Data (IPD) Sharing Statement:

Yes

Plan to Share IPD:

Yes

Studies a U.S. FDA-regulated Drug Product:

Studies a U.S. FDA-regulated Device Product:

Additional relevant MeSH terms:

Intracranial Hypertension

Study Results

No Results Posted as of Feb 16, 2023