Intracranial Pressure Increase
Conditions
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.
Interventions
DIAGNOSTIC_TESTMachine Learning Model
Automated machine learning system for the detection of spontaneous venous pulsations and quantification of intracranial pressure
Sponsors
King's College London
Study design
Observational model
COHORT
Time perspective
RETROSPECTIVE
Eligibility
Sex/Gender
ALL
Age
18 Years to No maximum
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.
Design outcomes
Primary
| Measure | Time frame | Description |
|---|---|---|
| Area-under-the receiver operating characteristic (AUROC) for spontaneous venous pulsations detection | 1 year | Binary classification performance of the machine learning model |
Secondary
| Measure | Time frame | Description |
|---|---|---|
| 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 |
Countries
United Kingdom
Outcome results
None listed