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This proof-of-concept study investigated whether multimodal retinal imaging\u2014hyperspectral imaging (HSI), optical coherence tomography (OCT), and color fundus photography (CFP)\u2014can differentiate individuals with and without cerebral amyloid-beta (A\u03b2) pathology in 40 participants with PET-confirmed A\u03b2 status (17 A\u03b2+, cognitively normal or with mild cognitive impairment; 23 A\u03b2\u2212 cognitively normal controls). HSI-derived gray-level co-occurrence matrix (GLCM) texture, OCT-derived ganglion cell\u2013inner 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 A\u03b2+ 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 A\u03b2+ 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.<\/jats:p>","DOI":"10.3390\/bioengineering13091041","type":"journal-article","created":{"date-parts":[[2026,9,8]],"date-time":"2026-09-08T08:10:22Z","timestamp":1788855022000},"page":"1041","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Multimodal Retinal Imaging for the Detection of Early Alzheimer\u2019s Disease: Combining Biochemical, Structural, and Vascular Biomarkers"],"prefix":"10.3390","volume":"13","author":[{"ORCID":"https:\/\/orcid.org\/0009-0006-1476-2761","authenticated-orcid":false,"given":"Michiel","family":"Ghesquiere","sequence":"first","affiliation":[{"name":"Research Group Ophthalmology, Department of Neurosciences, KU Leuven, ON5 Herestraat 49, 3000 Leuven, Belgium"},{"name":"Dynamical Systems, Signal Processing and Data Analytics (STADIUS), Department of Electrical Engineering (ESAT), KU Leuven, Kasteelpark Arenberg 10, 3001 Leuven, Belgium"}],"role":[{"vocabulary":"crossref","role":"author"},{"role":"software","vocabulary":"credit"},{"role":"methodology","vocabulary":"credit"},{"role":"writing-original-draft","vocabulary":"credit"},{"role":"formal-analysis","vocabulary":"credit"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5785-1509","authenticated-orcid":false,"given":"Eirini","family":"Christinaki","sequence":"additional","affiliation":[{"name":"Research Group Ophthalmology, Department of Neurosciences, KU Leuven, ON5 Herestraat 49, 3000 Leuven, 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