Multimodal Retinal Imaging for the Detection of Early Alzheimer's Disease: Combining Biochemical, Structural, and Vascular Biomarkers.

Alzheimer's disease (AD) pathology is increasingly recognized to manifest in the retina, offering a non-invasive window for early biomarker discovery. This proof-of-concept study investigated whether multimodal retinal imaging-hyperspectral imaging (HSI), optical coherence tomography (OCT), and color fundus photography (CFP)-can differentiate individuals with and without cerebral amyloid-beta (Aβ) pathology in 40 participants with PET-confirmed Aβ status (17 Aβ+, cognitively normal or with mild cognitive impairment; 23 Aβ- cognitively normal controls). HSI-derived gray-level co-occurrence matrix (GLCM) texture, OCT-derived ganglion cell-inner plexiform layer (GC-IPL) thickness, and CFP-derived vascular biomarkers (VBMs) were extracted, and logistic regression with leave-one-out cross-validation assessed classification performance per modality, alone and combined; the cohort was supplemented with AD dementia patients for an exploratory cross-sectional comparison across disease-stage groups. HSI showed nominally lower GLCM correlation at 466 nm in Aβ+ participants, most pronounced in the inferior macula (AUC = 0.72). GC-IPL thickness showed a similar inferior-predominant regional pattern. Combining HSI and GC-IPL features yielded the best performance (AUC = 0.84; sensitivity = 0.82; specificity = 0.78), although this improvement over the unimodal models did not reach statistical significance, whereas vascular biomarkers contributed minimally. In an exploratory cross-sectional comparison across AD stage groups drawn from two cohorts, HSI features showed a non-monotonic pattern, decreasing in early Aβ+ stages and rising again in dementia. These findings provide preliminary evidence of complementary information between HSI and OCT for detecting retinal biomarkers of early-stage AD, supporting multimodal retinal imaging as a scalable screening approach warranting validation in larger, longitudinal cohorts.