Early Alzheimer's disease detection from retinal OCT images: a UK Biobank study

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dc.authorid0000-0001-6309-4524
dc.authorid0000-0002-8649-6013
dc.authorid0009-0007-4212-5484
dc.authorid0009-0000-6524-5488
dc.contributor.authorTurkan, Yaseminen_US
dc.contributor.authorTek, Faik Borayen_US
dc.contributor.authorNazlı, M. Serdaren_US
dc.contributor.authorEren, Öyküen_US
dc.date.accessioned2026-05-05T12:52:09Z
dc.date.available2026-05-05T12:52:09Z
dc.date.issued2025-11-07
dc.departmentIşık Üniversitesi, Mühendislik ve Doğa Bilimleri Fakültesi, Matematik Bölümüen_US
dc.departmentIşık University, Faculty of Engineering and Natural Sciences, Department of Mathematicsen_US
dc.descriptionThis study was conducted using the UK Biobank Resource under Application Number 82266. Computational resources were provided by the Turkish National High-Performance Computing Center (UHeM, Project Number 1017802024). This study was also supported by the Scientific and Technological Research Council of Turkey (TUBITAK) under Grant Number 122E509.en_US
dc.description.abstractAlterations in retinal layer thickness, measurable using Optical Coherence Tomography (OCT), have been associated with neurodegenerative diseases such as Alzheimer's disease (AD). While previous studies have mainly focused on segmented layer thickness measurements, this study explored the direct classification of OCT B-scan images for the early detection of AD. To our knowledge, this is the first application of deep learning to raw OCT B-scans for AD prediction in the literature. Unlike conventional medical image classification tasks, early detection is more challenging than diagnosis because imaging precedes clinical diagnosis by several years. We fine-tuned and evaluated multiple pretrained models, including ImageNet-based networks and the OCT-specific RETFound transformer, using subject-level cross-validation datasets matched for age, sex, and imaging instances from the UK Biobank cohort. To reduce overfitting in this small, high-dimensional dataset, both standard and OCT-specific augmentation techniques were applied, along with a year-weighted loss function that prioritized cases diagnosed within four years of imaging. ResNet-34 produced the most stable results, achieving an AUC of 0.62 in the 4-year cohort. Although below the threshold for clinical application, our explainability analyses confirmed localized structural differences in the central macular subfield between the AD and control groups. These findings provide a baseline for OCT-based AD prediction, highlight the challenges of detecting subtle retinal biomarkers years before AD diagnosis, and point to the need for larger datasets and multimodal approaches.en_US
dc.description.sponsorshipTurkish National High-Performance Computing Center (UHeM)en_US
dc.description.sponsorshipScientific and Technological Research Council of Turkey (TUBITAK)en_US
dc.description.versionPreprint's Versionen_US
dc.identifier.citationTurkan, Y., Tek, F. B., Nazlı, M. S. & Eren, Ö. (2025). Early Alzheimer's disease detection from retinal OCT images: a UK Biobank study. Arxiv, 1-6. doi: https://doi.org/10.48550/arXiv.2511.05106en_US
dc.identifier.endpage6
dc.identifier.startpage1
dc.identifier.urihttps://hdl.handle.net/11729/7376
dc.identifier.urihttps://doi.org/10.48550/arXiv.2511.05106
dc.identifier.wosPPRN:161584711
dc.identifier.wosqualityN/A
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakPreprint Citation Indexen_US
dc.institutionauthorTurkan, Yaseminen_US
dc.institutionauthorid0000-0001-6309-4524
dc.language.isoenen_US
dc.publisherCornell Univen_US
dc.relation.ispartofArxiven_US
dc.relation.publicationcategoryÖn Baskı – Uluslararası – Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.titleEarly Alzheimer's disease detection from retinal OCT images: a UK Biobank studyen_US
dc.typePreprinten_US
dspace.entity.typePublicationen_US

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