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Non-invasive screening of alzheimer’s disease via label-free tri-spectral retinal imaging
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  • Published: 13 January 2026

Non-invasive screening of alzheimer’s disease via label-free tri-spectral retinal imaging

  • Zita Salajková1,2,
  • Gabriele Ciasca3,4,
  • Francesco Di Lorenzo5,
  • Maryamsadat Ghoreishi2,
  • Riccardo Reale6,
  • Maria Grazia Gambarota1,
  • Jianping Zhang7,
  • Yanxing Zhang8,
  • Vincenzo Ricco2,
  • Giancarlo Ruocco1,9 &
  • …
  • Marco Leonetti1,2,10 

Scientific Reports , Article number:  (2026) Cite this article

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We are providing an unedited version of this manuscript to give early access to its findings. Before final publication, the manuscript will undergo further editing. Please note there may be errors present which affect the content, and all legal disclaimers apply.

Subjects

  • Biomarkers
  • Computational biology and bioinformatics
  • Diseases
  • Engineering
  • Health care
  • Medical research

Abstract

Alzheimer’s disease (AD) is the most prevalent form of dementia, yet its early detection remains challenging due to the invasiveness, cost, and limited accessibility of current diagnostics. Increasing evidence suggests that retinal changes mirror cerebral pathology in AD, making the eye a promising site for non-invasive biomarker discovery. Here, we present a technique employing a custom-built tri-spectral retinal imaging module, designed to be integrated with existing fundus imaging systems, that captures retinal reflectance across three optimized spectral bands to quantify spectral alterations linked to AD. We validate the system in a case-control study of 38 mild AD patients and 28 age-matched controls, revealing spatially resolved differences in a fundus map derived from the blue-to-green ratiometric channel (p < 0.001). Our analysis identifies specifically the fovea-to-optic disc region as the most discriminative for AD, with an AUC of 0.74. Building on this, we developed a biologically informed machine-learning classification model incorporating spectral, clinical, and demographic data. On an independent validation test, the model achieved an AUC of 0.91, matching or slightly outperforming the most advanced spectral retinal measurements, yet using a simpler, more stable, and cost-effective setup that further facilitates clinical translation. The demonstrated technology, thanks to its non-invasiveness and its integrability with both existing medical technologies and advanced quantitative statistical methods, holds the potential to drive a significant leap forward in the early detection of AD, opening a window for timely intervention and thus profoundly impacting patient care.

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Data availability

De-identified participant data (including age, sex, ocular history, and spectral imaging values) and MATLAB/R analysis code are available from the corresponding author upon reasonable request. Due to privacy and ethical restrictions, raw retinal images cannot be publicly shared but may be provided in anonymized form for academic collaborations.

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Acknowledgements

The authors declare no competing financial or non-financial interests related to this work.

Funding

This work was supported by MUR PRIN 2022 (CUP: 2022CFP7RF, to M.L.). This research was also funded by the D-Tails-IIT Joint Lab, GR is supported by Project “National Center for Gene Therapy and Drugs based on RNA Technology” (CN00000041) financed by NextGenerationEU PNRR MUR— M4C2—Action 1.4-Call “Potenziamento strutture di ricerca e creazione di “campioni nazionali di R\&S” (CUP J33C22001130001)”. The research leading to these results was also supported by European Research Council through its Synergy grant program, project ASTRA (grant agreement No. 855923) and by European Innovation Council through its Pathfinder Open Program, project ivBM-4PAP (grant agreement No. 101098989). The authors acknowledge the support of the Italian Fund for Applied Sciences (FISA), project "ROAD- Retina Observation for Alzheimer Diagnostic", funded by the Italian Ministry of University and Research (MUR), under Call No. 1233 of 01/08/2023.

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Authors and Affiliations

  1. Center for Life Nano- and Neuro-Science, Istituto Italiano di Tecnologia, Viale Regina Elena 291, Rome, 00161, Italy

    Zita Salajková, Maria Grazia Gambarota, Giancarlo Ruocco & Marco Leonetti

  2. D-Tails s.r.l., Via Agrigento, 4/B, Rome, 00161, Italy

    Zita Salajková, Maryamsadat Ghoreishi, Vincenzo Ricco & Marco Leonetti

  3. Dipartimento di Neuroscienze, Sezione di Fisica, Università Cattolica del Sacro Cuore, Rome, 00168, Italy

    Gabriele Ciasca

  4. Fondazione Policlinico Universitario Agostino Gemelli I.R.C.C.S., Rome, 00168, Italy

    Gabriele Ciasca

  5. 5Non-invasive Brain Stimulation Unit, IRCCS Fondazione Santa Lucia, Rome, Italy

    Francesco Di Lorenzo

  6. Department of Civil Engineering and Computer Science, University of Rome Tor Vergata, Rome, Italy

    Riccardo Reale

  7. JAR Innovation Center, No.199 Chuangyi Road, Keqiao, 312030, Shaoxing, China

    Jianping Zhang

  8. Department of Neurology, Shaoxing People’s Hospital, The First Hospital of Shaoxing University, No.568 Zhongxing North Road, Shaoxing, 312000, Zhejiang, China

    Yanxing Zhang

  9. Dipartimento di Fisica, Università di Roma “La Sapienza”, P.le Aldo Moro 5, Roma, I-00185, Italy

    Giancarlo Ruocco

  10. Institute of Nanotechnology of the National Research Council of Italy, CNR-NANOTEC, Rome Unit, Piazzale A. Moro 5, Rome, I-00185, Italy

    Marco Leonetti

Authors
  1. Zita Salajková
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Contributions

Conceptualization: ZS, ML, VR, GR Methodology: ZS, MLInvestigation: ZS, GC, MsGVisualization: ZSFunding acquisition: GR, VRProject administration: ZSSupervision: ZS, MLWriting – original draft: ZS, GCWriting – review & editing: FDL, RR, ML, MsG, MgG, JZ, YZ.

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Correspondence to Zita Salajková.

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Salajková, Z., Ciasca, G., Di Lorenzo, F. et al. Non-invasive screening of alzheimer’s disease via label-free tri-spectral retinal imaging. Sci Rep (2026). https://doi.org/10.1038/s41598-026-35383-y

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  • Received: 19 August 2025

  • Accepted: 05 January 2026

  • Published: 13 January 2026

  • DOI: https://doi.org/10.1038/s41598-026-35383-y

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Keywords

  • Alzheimer’s disease
  • Multispectral retinal imaging
  • Retinal biomarkers
  • Fundus photography
  • Non-invasive diagnostics
  • Optical biosensors
  • Dementia screening
  • Machine learning
  • XGBoost
  • SHAP analysis
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