Hyperspectral retinal imaging as a non-invasive marker to determine brain amyloid status

Document Type

Journal Article

Publication Title

Journal of Alzheimer's Disease : JAD

Volume

100

Issue

s1

First Page

S131

Last Page

S152

PubMed ID

39121128

Publisher

IOS Press

School

Centre of Excellence for Alzheimer's Disease Research and Care / School of Medical and Health Sciences

Comments

Poudel, P., Frost, S. M., Eslick, S., Sohrabi, H. R., Taddei, K., Martins, R. N., & Hone, E. (2024). Hyperspectral retinal imaging as a non-invasive marker to determine brain amyloid status. Journal of Alzheimer's Disease, 100(s1), S131-S152. https://doi.org/10.3233/JAD-240631

Abstract

Background: As an extension of the central nervous system (CNS), the retina shares many similarities with the brain and can manifest signs of various neurological diseases, including Alzheimer's disease (AD). Objective: To investigate the retinal spectral features and develop a classification model to differentiate individuals with different brain amyloid levels. Methods: Sixty-six participants with varying brain amyloid-β protein levels were non-invasively imaged using a hyperspectral retinal camera in the wavelength range of 450-900 nm in 5 nm steps. Multiple retina features from the central and superior views were selected and analyzed to identify their variability among individuals with different brain amyloid loads. Results: The retinal reflectance spectra in the 450-585 nm wavelengths exhibited a significant difference in individuals with increasing brain amyloid. The retinal features in the superior view showed higher inter-subject variability. A classification model was trained to differentiate individuals with varying amyloid levels using the spectra of extracted retinal features. The performance of the spectral classification model was dependent upon retinal features and showed 0.758-0.879 accuracy, 0.718-0.909 sensitivity, 0.764-0.912 specificity, and 0.745-0.891 area under curve for the right eye. Conclusions: This study highlights the spectral variation of retinal features associated with brain amyloid loads. It also demonstrates the feasibility of the retinal hyperspectral imaging technique as a potential method to identify individuals in the preclinical phase of AD as an inexpensive alternative to brain imaging.

DOI

10.3233/JAD-240631

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