SVP Detection Using Machine Learning

Intracranial Pressure Increase Clinical Trial

— SVP-ML  

Official title:

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

Clinical Trial Details — Status: Active, not recruiting

Administrative data

• NCT number: NCT05731765
• Other study ID #: 1.0
• Status: Active, not recruiting
• Start date: March 1, 2023
• Est. completion date: November 2024

Study information

• Verified date: March 2024
• Source: King's College London
• Contact: n/a
• Is FDA regulated: No
• Study type: Observational

Clinical Trial 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.

Recruitment information / eligibility

• Status: Active, not recruiting
• Enrollment: 210
• Est. completion date: November 2024
• Est. primary completion date: November 2024
• Accepts healthy volunteers: Accepts Healthy Volunteers
• Gender: All
• Age group: 18 Years and older

Eligibility

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.

Related Conditions & MeSH terms

• Intracranial Hypertension
• Intracranial Pressure Increase

Intervention

Diagnostic Test:
• Machine Learning Model
Automated machine learning system for the detection of spontaneous venous pulsations and quantification of intracranial pressure

Locations

Country	Name	City	State
United Kingdom	King's College London	London

Sponsors (1)

Lead Sponsor	Collaborator
King's College London

Country where clinical trial is conducted

United Kingdom, 

Outcome

Type	Measure	Description	Time frame	Safety issue
Primary	Area-under-the receiver operating characteristic (AUROC) for spontaneous venous pulsations detection	Binary classification performance of the machine learning model	1 year
Secondary	Localisation of spontaneous venous pulsations	Bounding box overlap for the machine learning model	1 year
Secondary	Quantification of intracranial pressure	Mean absolute error for the prediction of the intracranial pressure	1 year