Defining Retinal Structures Using Hyperspectral Retinal Imaging

Condition(s) studied

Investigational drug(s) / intervention(s)

Hyperspectral 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.

Study 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.

Eligibility

Primary outcome measure(s)

• 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.

Trial sites (1)

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