SVP Detection Using Machine Learning

Active, not recruiting

• Conditions: Intracranial Pressure Increase
• Interventions: Diagnostic Test: Machine Learning Model

• Registration Number: NCT05731765
• Lead Sponsor: King's College London

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.

Detailed Description

Not available

Study Timeline

• Study First Submitted: 2023-01-30
• Study First Submitted QC: 2023-02-07
• Study First Posted: 2023-02-16
• Study Start: 2023-03-01
• Status Verified: 2024-03
• Last Update Submitted: 2024-03-05
• Last Update Posted: 2024-03-06
• Primary Completion: 2024-11
• Study Completion: 2024-11

Recruitment & Eligibility

• Status: ACTIVE_NOT_RECRUITING
• Sex: All
• Target Recruitment: 210

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 & Design

• Study Type: OBSERVATIONAL
• Study Design: Not specified

Arm && Interventions

Group	Intervention	Description
Patients aged ≥18 years with suspected raised intracranial pressure	Machine Learning Model	-
Patients aged ≥18 years with presumed normal intracranial pressure	Machine Learning Model	-

Primary Outcome Measures

Name	Time	Method
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

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

Trial Locations

Locations (1)

King's College London; 🇬🇧 London, United Kingdom