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This study evaluates hyperspectral retinal imaging as a novel, non-invasive imaging technique to characterise retinal and optic nerve structures in healthy individuals and patients with eye disease. Hyperspectral imaging captures retinal data across multiple wavelengths to generate detailed spectral information that may reveal features not visible with conventional retinal photography.
Approximately 1000 participants will undergo multi-modal ophthalmic imaging in Melbourne, Australia, including hyperspectral imaging, OCT, fundus photography, and related tests. The study aims to compare hyperspectral imaging with standard imaging methods and assess its ability to identify retinal biomarkers associated with diseases such as diabetic retinopathy, glaucoma, and age-related macular degeneration.
This is a investigator-initiated imaging study assessing hyperspectral retinal imaging (HSI) for characterising retinal and optic nerve structures in healthy and diseased eyes.
HSI acquires retinal images across multiple wavelengths (>25 bands), producing a spectral "hypercube" containing spatial and spectral information for each pixel. This provides more detailed tissue information than conventional colour fundus photography.
Approximately 1000 participants will be recruited from ophthalmology clinics in Melbourne. All participants will undergo standard ophthalmic assessment and hyperspectral retinal imaging using the Optina device and a CERA prototype camera.
Hyperspectral images will be processed with registration and spectral normalisation to extract pixel-level reflectance signatures. These data will be analysed using statistical and machine learning methods and compared with established imaging biomarkers to evaluate their ability to distinguish disease states.
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| Label | Type | Description | Intervention Names |
|---|---|---|---|
| Hyperspectral retinal imaging | Experimental | Participants undergo non-invasive hyperspectral retinal imaging as part of a multi-modal ophthalmic imaging protocol. Hyperspectral imaging is performed using the Optina Diagnostics Metabolic Hyperspectral Retinal Camera and/or a prototype hyperspectral camera developed at the Centre for Eye Research Australia. The intervention involves sequential retinal imaging across multiple wavelengths (>25 spectral bands) following standard dilated fundus examination. Imaging is similar to conventional retinal photography but provides additional spectral information for each retinal pixel. Participants also undergo standard clinical imaging (including OCT, OCT angiography, fundus photography, and autofluorescence) for comparison. The study is observational and non-therapeutic, with hyperspectral imaging used solely for research analysis of retinal structure and spectral biomarkers. |
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| Name | Type | Description | Arm Group Labels | Other Names |
|---|---|---|---|---|
| Hyperspectral camera | Device | Hyperspectral imaging is performed with the Metabolic Hyperspectral Retinal Camera (Optina Diagnostic, Montreal, Canada) and a prototype camera developed by researchers at the Centre for Eye Research Australia (CERA). The Metabolic Hyperspectral Retinal Camera is similar to a typical fundus imager but it incorporates a tunable light source which is able to transmit safe light levels within a wavelength range covering the visible to near infrared with a narrow bandwidth (< 3nm). This instrument is capable of imaging a 26° field-of-view of retina at 90 wavelengths in less than a second, thus minimizing discomfort and limiting the influence of eye movements. The hyperspectral camera developed by CERA researchers is a non-mydriatic fundus camera that uses light emitting diodes (LEDs) and an optical variable bandpass filter to tune the illumination wavelengths. |
| Measure | Description | Time Frame |
|---|---|---|
| Diagnostic performance of hyperspectral imaging-derived spectral score for detection of retinal disease | To assess the ability of hyperspectral retinal imaging to distinguish between healthy and diseased eyes using a quantitative hyperspectral spectral score derived from image analysis algorithms (e.g., DROP-D or machine learning models). Diagnostic performance will be evaluated against clinical diagnosis using receiver operating characteristic (ROC) analysis. | During study visit (baseline data collection); analyses performed after completion of participant recruitment and imaging dataset acquisition |
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Inclusion Criteria:
Exclusion Criteria:
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| Name | Role | Phone | Extension | |
|---|---|---|---|---|
| Darvy Dang | Contact | +61 3 9959 0102 | darvy.dang@unimelb.edu.au |
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| Facility | Status | City | State | ZIP | Country | Contacts |
|---|---|---|---|---|---|---|
| The Centre for Eye Research Australia | Recruiting | Melbourne | Victoria | 3002 | Australia |
The plan for sharing individual participant data (IPD) has not yet been finalised. This is a multi-modal imaging study involving large hyperspectral and ophthalmic imaging datasets, and decisions regarding data sharing will depend on completion of primary analyses, data governance requirements, and ethical approvals. The feasibility of appropriate de-identification and secure sharing mechanisms is under review.
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| ID | Term |
|---|---|
| D008268 | Macular Degeneration |
| D003930 | Diabetic Retinopathy |
| D005901 | Glaucoma |
| D012164 | Retinal Diseases |
| ID | Term |
|---|---|
| D012162 | Retinal Degeneration |
| D005128 | Eye Diseases |
| D003925 | Diabetic Angiopathies |
| D014652 | Vascular Diseases |
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|
| D002318 |
| Cardiovascular Diseases |
| D048909 | Diabetes Complications |
| D003920 | Diabetes Mellitus |
| D004700 | Endocrine System Diseases |
| D009798 | Ocular Hypertension |