IntroductionThis 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.MethodsRetinal 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.ResultsRetinal 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%.DiscussionSignificant 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.

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Background: As the only optically accessible part of the central nervous system, the retina represents an intriguing opportunity for the detection of biomarkers for Alzheimer's disease (AD). This study evaluated the performance of the Retinal Deep Phenotyping TM platform, a digital biomarker platform comprising a hyperspectral retinal camera and image analysis algorithms, for the detection of likely positron-emission tomography (PET) amyloid status (negative or positive) in older adults. A set of phenotypic features that correlates with the cerebral amyloid status as determined by amyloid PET scan were identified and used to train a classifying algorithm.Method: Hyperspectral retinal images acquired with a Mydriatic Hyperspectral Retinal Camera from 194 participants (age ≥ 50 years), including cognitively normal and cognitively impaired (mild cognitive impairment and dementia) across 5 imaging sites were processed in order to train the model. Of these 194 participants, 73 individuals (38%) were amyloid-positive, as confirmed by unanimous readings of PET scans by a panel of 3 expert reviewers. The pre-processed hyperspectral images were segmented into various anatomical sites, and a texture-based approach was used to extract several thousands of spatial-spectral features. The most relevant features for the classification task were selected using a minimum redundancy maximum relevance (MRMR) algorithm and used to train a linear support vector machine (SVM) classifier. A nested, cross-validation technique was used to evaluate the performance of the classifier. Result:The resulting model based on the 17 most significant features showed high performance to discriminate between amyloid positive and negative subjects with an area under the receiver operating curve (AUC ROC ) of 0.87 (95% CI: 0.83 -0.92).