SVP Detection Using Machine Learning (SVP-ML)

King's College London

Status: Active, not recruiting

Conditions: Intracranial Pressure Increase

Treatments: Diagnostic Test: Machine Learning Model

Study type: Observational

Funder types: Other

Identifiers

NCT05731765

1.0

Details and patient eligibility

About

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.

Enrollment: 210 estimated patients

Sex: All

Ages: 18+ years old

Volunteers: Accepts Healthy Volunteers

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.

Trial design

210 participants in 2 patient groups

Patients aged ≥18 years with presumed normal intracranial pressure

Treatment:

Diagnostic Test: Machine Learning Model

Patients aged ≥18 years with suspected raised intracranial pressure

Treatment:

Diagnostic Test: Machine Learning Model