Vascular retinal biomarkers improves the detection of the likely cerebral amyloid status from hyperspectral retinal images

Abstract

Introduction: This study investigates the relationship between retinal image features and β-amyloid (Aβ) burden in the brain with the aim of developing a noninvasive method to predict the deposition of Aβ in the brain of patients with Alzheimer's disease.

Methods: Retinal images from 20 cognitively impaired and 26 cognitively unimpaired cases were acquired (3 images per subject) using a hyperspectral retinal camera. The cerebral amyloid status was determined from binary reads by a panel of 3 expert raters on ¹⁸F-florbetaben positron-emission tomography (PET) studies. Image features from the hyperspectral retinal images were calculated, including vessels tortuosity and diameter and spatial-spectral texture measures in different retinal anatomical regions.

Results: Retinal venules of amyloid-positive subjects (Aβ+) showed a higher mean tortuosity compared with the amyloid-negative (Aβ-) subjects. Arteriolar diameter of Aβ+ subjects was found to be higher than the Aβ- subjects in a zone adjacent to the optical nerve head. Furthermore, a significant difference between texture measures built over retinal arterioles and their adjacent regions were observed in Aβ+ subjects when compared with the Aβ-. A classifier was trained to automatically discriminate subjects combining the extracted features. The classifier could discern Aβ+ subjects from Aβ- subjects with an accuracy of 85%.

Discussion: Significant differences in texture measures were observed in the spectral range 450 to 550 nm which is known as the spectral region known to be affected by scattering from amyloid aggregates in the retina. This study suggests that the inclusion of metrics related to the retinal vasculature and tissue-related textures extracted from vessels and surrounding regions could improve the discrimination performance of the cerebral amyloid status.

Keywords: Alzheimer; Beta amyloid; Image processing; Machine learning; Multispectral fundus imaging; Retina.

Fig. 1

Schematic of the metabolic hyperspectral retinal camera used for acquiring the images.

Fig. 2

(A) Tortuosity measurement steps of a sample vessel segment. (B) Different zones of retina: zone A (region from 0 to 0.5 ONH diameters away from the ONH margin), zone B (region from 0.5 to 1 ONH diameters away from the ONH margin), and zone C (region from 0.5 to 2 ONH diameters away from the ONH margin).

Fig. 3

Difference between β-amyloid-positive and β-amyloid-negative data sets corresponding to features F1 (A) to F8 (H)—Data points are shown as normalized values.

Fig. 4

(A) SVM classification loss using principal components 1 to 8 inclusively; (B) comparison of classification losses when using different types of features.