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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.
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| Label | Type | Description | Intervention Names |
|---|---|---|---|
| Patients aged ≥18 years with presumed normal intracranial pressure |
| ||
| Patients aged ≥18 years with suspected raised intracranial pressure |
|
| Name | Type | Description | Arm Group Labels | Other Names |
|---|---|---|---|---|
| Machine Learning Model | Diagnostic Test | Automated machine learning system for the detection of spontaneous venous pulsations and quantification of intracranial pressure |
|
| Measure | Description | Time Frame |
|---|---|---|
| Area-under-the receiver operating characteristic (AUROC) for spontaneous venous pulsations detection | Binary classification performance of the machine learning model | 1 year |
| Measure | Description | Time Frame |
|---|---|---|
| Localisation of spontaneous venous pulsations | Bounding box overlap for the machine learning model | 1 year |
| Quantification of intracranial pressure | Mean absolute error for the prediction of the intracranial pressure |
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Inclusion Criteria:
Exclusion Criteria:
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Patients aged ≥18 years with presumed normal ICP or suspected raised ICP
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| Facility | Status | City | State | ZIP | Country | Contacts |
|---|---|---|---|---|---|---|
| King's College London | London | United Kingdom |
The data would be shared, where possible, through a restricted-access data sharing agreement, where in line with KCL data governance requirements.
Within 12 months of study completion
Data sharing agreement and data governance
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| ID | Term |
|---|---|
| D019586 | Intracranial Hypertension |
| ID | Term |
|---|---|
| D001927 | Brain Diseases |
| D002493 | Central Nervous System Diseases |
| D009422 | Nervous System Diseases |
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| 1 year |