Machine learning for comprehensive prediction of high risk for Alzheimer's disease based on chromatic pupilloperimetry.

ABSTRACT: Currently there are no reliable biomarkers for early detection of Alzheimer's disease (AD) at the preclinical stage. This study assessed the pupil light reflex (PLR) for focal red and blue light stimuli in central and peripheral retina in 125 cognitively normal middle age subjects (45-71 years old) at high risk for AD due to a family history of the disease (FH+), and 61 age-similar subjects with no family history of AD (FH-) using Chromatic Pupilloperimetry coupled with Machine Learning (ML). All subjects had normal ophthalmic assessment, and normal retinal and optic nerve thickness by optical coherence tomography. No significant differences were observed between groups in cognitive function and volumetric brain MRI. Chromatic pupilloperimetry-based ML models were highly discriminative in

SUBMITTER: Lustig-Barzelay Y 

PROVIDER: S-EPMC9200977 | biostudies-literature | 2022 Jun

REPOSITORIES: biostudies-literature