Age-Related Macular Degeneration, Diabetic Retinopathy, Glaucoma, Healthy Volunteers, Retinal Diseases
Conditions
Brief summary
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.
Detailed description
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.
Interventions
DEVICEHyperspectral camera
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.
Sponsors
Center for Eye Research Australia
Study design
Allocation
NA
Intervention model
SINGLE_GROUP
Primary purpose
DIAGNOSTIC
Masking
NONE
Eligibility
Sex/Gender
ALL
Age
18 Years to No maximum
Healthy volunteers
Yes
Inclusion criteria
* Adults aged 18 years and older * Able to provide informed consent * Willing and able to attend a study visit at the Centre for Eye Research Australia * Participants with diagnosed retinal or optic nerve disease (e.g., diabetic retinopathy, glaucoma, age-related macular degeneration) * Age- and sex-matched healthy control participants without known retinal or optic nerve disease
Exclusion criteria
* Inability to provide informed consent * Ocular conditions preventing adequate retinal imaging (e.g., dense cataract, severe corneal opacity, vitreous haemorrhage) * Known contraindication to pharmacological pupil dilation * History of narrow anterior chamber angle or risk of angle closure glaucoma where dilation is considered unsafe * Any condition that, in the investigator's opinion, would compromise participant safety or image quality
Design outcomes
Primary
| Measure | Time frame | Description |
|---|---|---|
| Diagnostic performance of hyperspectral imaging-derived spectral score for detection of retinal disease | During study visit (baseline data collection); analyses performed after completion of participant recruitment and imaging dataset acquisition | 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. |
Countries
Australia
Contacts
CONTACTDarvy Dang
Outcome results
None listed