Retinal Diseases

The purpose of this prospective, longitudinal, single-center observational study is to identify, describe, and measure the morpho-functional characteristics and modifications of the retina and choroid using advanced multimodal imaging techniques. The study will evaluate patients affected by retino-choroidal diseases and compare them with healthy controls over a 5-year follow-up period to track the evolution of these parameters over time.

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.

The study team will conduct the first in-human pilot studies using 4D MIOCT for imaging in the operating room. Building on the earlier integration and wet-lab validations, the researchers will assess the system's ease of use, safety, and visualization of intended structures during various anterior and posterior segment ophthalmic procedures.

AMD is a leading cause of blindness in individuals over 50 years old, with dry AMD being the most common form. Geographic atrophy (GA) is an advanced stage of dry AMD characterized by progressive retinal cell degeneration. The primary objectives of the study are to assess the safety, tolerability, and evidence of activity of SGLT2 inhibitors in subjects with Geographic Atrophy associated with AMD.

Optical coherence tomography (OCT )provides high resolution information regarding the anatomic structure of the tissues of the eye in a cross-sectional and 3 dimensional view. Much of this information is not able to be visualized by a clinician. Utilizing this information during surgery will allow for the ophthalmic surgeons to better understand how surgical procedures impact the anatomic structure of the eye. In this study an OCT device that has been built into the microscope (rather than mounted on the side or held in the surgeon's hand) and will be utilized to take images at various milestones during surgery to assess feasibility and potential utility of this technology. Since it is built into the microscope, there are potential significant advantages over a separate system including increased efficiency, improved working distance, and the ability to visualize tissue-instrument interactions.

The MRI perfusion imaging using arterial spin labeling (ASL)'s non-invasive technique (i.e. without injection of contrast medium) allows, thanks to recent technological improvement of the spatial resolution, measuring several perfusion parameter of the retinal tissue such as the regional Blood Flow (rBF) or the cerebral blood flow (cBF) expressed in ml/100g/min. Reliable application of ASL thus requires the precision and specificity of the MRI protocol to be tested.

To establish a multimodal fundus image report generation model to realize an interpretable system for multiple fundus diseases, multimodal image analysis, diagnosis, and treatment decision automatic reporting based on weakly labeled training data. Construct an interpretable feature fusion network for the clinical and imaging features of fundus lesions, and we hope to extract new imaging markers that can predict the occurrence and progression of various fundus lesions at an early stage, and ultimately verify them in real clinical data, further providing possible directions for exploring the molecular mechanisms of refractory fundus lesions, and may also provide new ideas for the precise prevention and treatment of fundus lesions.

The objective of this study is to apply an artificial intelligence algorithm to diagnose multi-retinal diseases in real-world settings. The effectiveness and accuracy of this algorithm are evaluated by sensitivity, specificity, positive predictive value, negative predictive value, and area under curve.