SVP Detection Using Machine Learning

NCT05731765 | Active Not Recruiting

• 1 other identifier: 1.0
• Study Type: observational
• Target: 210
• Locations: 1 country
• Sites: 1

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.

Conditions

Intracranial Pressure Increase

Outcome Measures

Primary Outcomes (1)

• Area-under-the receiver operating characteristic (AUROC) for spontaneous venous pulsations detection

  Binary classification performance of the machine learning model

  1 year

Secondary Outcomes (2)

• Localisation of spontaneous venous pulsations

  1 year

• Quantification of intracranial pressure

  1 year

Study Arms (2)

Patients aged ≥18 years with presumed normal intracranial pressure

Diagnostic Test: Machine Learning Model

Patients aged ≥18 years with suspected raised intracranial pressure

Diagnostic Test: Machine Learning Model

Interventions

Machine Learning Model DIAGNOSTIC_TEST

Automated machine learning system for the detection of spontaneous venous pulsations and quantification of intracranial pressure

Patients aged ≥18 years with presumed normal intracranial pressure; Patients aged ≥18 years with suspected raised intracranial pressure

Eligibility Criteria

• Age: 18 Years+
• Sex: all
• Healthy Volunteers: Yes
• Age Groups: Adult (18-64), Older Adult (65+)
• Sampling Method: Non-Probability Sample

Study Population

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

You may qualify if:

• 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.

You may not qualify if:

• Glaucoma diagnosis or glaucoma suspects in either eye.
• Bilateral restricted fundal view, e.g. advanced bilateral cataracts.
• Bilateral retinal vein or artery occlusion.

Sponsors & Collaborators

• King's College London: lead

Study Sites (1)

King's College London

London, United Kingdom

Location

MeSH Terms

Conditions

Intracranial Hypertension

Condition Hierarchy (Ancestors)

Brain Diseases; Central Nervous System Diseases; Nervous System Diseases

Study Design

• Study Type: observational
• Observational Model: COHORT
• Time Perspective: RETROSPECTIVE
• Sponsor Type: OTHER
• Responsible Party: SPONSOR

Study Record Dates

• First Submitted: January 30, 2023
• First Posted: February 16, 2023
• Study Start: March 1, 2023
• Primary Completion: November 1, 2024
• Study Completion: November 1, 2024
• Last Updated: March 6, 2024

Record last verified: 2024-03

Data Sharing

• IPD Sharing: Will share
• Shared Documents: STUDY PROTOCOL
• Time Frame: Within 12 months of study completion
• Access Criteria: Data sharing agreement and data governance

Locations

GB (1)