Early Alzheimer’s disease (AD) diagnosis before cognitive impairment occurs, could allow patients at the early stages of the disease to receive early intervention, prior to irreversible damage or cognitive decline. Such diagnosis is theoretically possible as AD neuropathology can be measured using invasive methods, years to decades before clinical symptoms appear.

Retinas of people with AD contain significantly different protein levels compared to healthy individuals. One of these proteins is amyloid beta (Aβ), which forms soluble Aβ (sAβ) aggregates in the central nervous system. sAβ has been increasingly recognized in recent years as a promising AD clinical biomarker that reflects early aggregation events. The RetiSpec Retinal Imaging System uses non-invasive retinal hyperspectral imaging (rHSI) to measure the Aβ spectral signature, which correlates with AD disease progression. rHSI constitutes high-resolution spectral-spatial imaging across a series of wavelengths; it combines these data-rich inputs with machine-learning and artificial intelligence (AI) algorithms to analyze observed changes in optical properties to detect Aβ signatures resulting from AD.

RetiSpec has developed its rHSI system to provide prognostic information regarding an individual’s likely PET brain amyloidosis status (PET positive or negative) to aid in the evaluation of adults for AD and other causes of cognitive decline. In this project, we propose to complete the activities required to support 510k clearance of RetiSpec’s Retinal Imaging System hardware (safety and bench testing, FDA filing), and conduct a pivotal study to establish the agreement of the RetiSpec Retinal Analysis Software with Aβ PET for the detection of Aβ PET status in individuals seeking AD clinical evaluation, to support a de-novo regulatory clearance for RetiSpec’s Retinal Analysis Software. Collectively, these activities can facilitate timely commercialization of RetiSpec’s Retinal Imaging System and Analysis Software, to enable a non-invasive detection of early signs of AD through scalable retinal imaging, enabling widespread population screening.