Retinal Biomarkers of Neurodegeneration: The Emerging Role of Optical Coherence Tomography (OCT) and Artificial Intelligence (AI)

Authors

  • Maria Calzón Álvarez Fernández-Vega Ophthalmology Insitute, Madrid, Spain. Autor/a

DOI:

https://doi.org/10.71413/rzy1dw04

Keywords:

Optical Coherence Tomography, Artificial Intelligence, Retinal Biomarkers, Neurodegeneration, Early Diagnosis

Abstract

Relevance: The significance of this approach lies in the retina’s potential as an accessible window into the neurodegeneration of the Central Nervous System. By combining OCT with AI, it is possible to identify abnormalities before symptoms emerge, facilitating the diagnosis of diseases such as Alzheimer’s and Parkinson’s.

Abstract: The retina functions as an extension of the Central Nervous System, enabling the observation of neurodegenerative processes. By combining Optical Coherence Tomography (OCT) with Artificial Intelligence (AI), it is possible to identify biomarkers that can improve the diagnosis and monitoring of diseases such as Alzheimer’s and Parkinson’s.

References

London A, Benhar I, Schwartz M. The retina as a window to the brain-from eye research to CNS disorders. Nat Rev Neurol. 2013;9(1):44-53. DOI: https://doi.org/10.1038/nrneurol.2012.227

Huang D, Swanson EA, Lin CP, et al. Optical coherence tomography. Science. 1991;254(5035):1178-1181. DOI: https://doi.org/10.1126/science.1957169

Kashani AH, Asanad S, Chan JW, et al. Past, present and future of optical coherence tomography angiography. Prog Retin Eye Res. 2021;83:100938. DOI: https://doi.org/10.1016/j.preteyeres.2020.100938

Ting DSW, Pasquale LR, Peng L, et al. Artificial intelligence and deep learning in ophthalmology. Br J Ophthalmol. 2019;103(2):167-175. DOI: https://doi.org/10.1136/bjophthalmol-2018-313173

Sijilmassi O. The retina as a proxy for brain neurodegeneration: a narrative review on OCT-based retinal imaging in the early detection of Alzheimer’s and Parkinson’s disease. J Imaging. 2022;8(3):64.

Yu JG, Feng YF, Xiang Y, et al. Retinal nerve fiber layer thickness changes in Parkinson disease: a meta-analysis. PLoS One. 2014;9(1):e85119. DOI: https://doi.org/10.1371/journal.pone.0085718

Satue M, Obis J, Rodrigo MJ, et al. Optical coherence tomography as a biomarker for diagnosis, progression, and prognosis of Parkinson’s disease. Front Neurol. 2019;10:1361.

Koronyo Y, Biggs D, Barron E, et al. Retinal amyloid-β plaques and neuroinflammation in anticipation of brain pathology in Alzheimer’s disease. JCI Insight. 2017;2(16):e93621. DOI: https://doi.org/10.1172/jci.insight.93621

Thomson KL, Yeo JM, Waddell B, et al. Optical coherence tomography outcomes in dementia: a meta-analysis. Alzheimers Dement (Amst). 2015;1(2):257-267. DOI: https://doi.org/10.1016/j.dadm.2015.03.001

Kirbas S, Turkyilmaz K, Anlar O, et al. Peripapillary retinal nerve fiber layer thickness in patients with Alzheimer disease. J Neuroophthalmol. 2013;33(1):58-61. DOI: https://doi.org/10.1097/WNO.0b013e318267fd5f

Garcia-Martin E, Bambo MP, Marques ML, et al. Ganglion cell layer thickness measured with macular VCC OCT in patients with Alzheimer’s disease. Eye (Lond). 2016;30(6):783-789.

Poewe W, Seppi K, Tanner CM, et al. Parkinson disease. Nat Rev Dis Primers. 2017;3:17013. DOI: https://doi.org/10.1038/nrdp.2017.13

Bodis-Wollner I, Kozlowski PB, Glazman S, et al. Retinal pathology in Parkinson’s disease. Parkinsonism Relat Disord. 2014;20 Suppl 1:S100-104.

Petzold A, de Boer JF, Schippling S, et al. Optical coherence tomography in multiple sclerosis: a systematic review and meta-analysis. Lancet Neurol. 2010;9(9):921-932. DOI: https://doi.org/10.1016/S1474-4422(10)70168-X

Martinez-Lapiscina EH, Arnow S, Mostert J, et al. Retinal thickness changes as a biomarker of neurodegeneration in multiple sclerosis: a 5-year longitudinal cohort study. Lancet Neurol. 2016;15(6):574-584. DOI: https://doi.org/10.1016/S1474-4422(16)00068-5

Kashani AH, Chen CL, Gahm JK, et al. Optical coherence tomography angiography: a comprehensive review of current methods and clinical applications. Prog Retin Eye Res. 2017;60:66-100. DOI: https://doi.org/10.1016/j.preteyeres.2017.07.002

Newman EA. Functional hyperemia in the retina: the role of astrocytes. J Neurosci. 2013;33(10):4216-4222.

Bulut M, Yaman A, Erol MK, et al. Choroidal thickness and retinal vascular density in interictal migraine patients without aura: an optical coherence tomography angiography study. J Neuroophthalmol. 2018;38(4):475-481.

Zhang YS, Zhou N, Knoll BM, et al. Parafoveal retinal vessel density in Alzheimer’s disease by optical coherence tomography angiography. J Alzheimers Dis. 2016;55(3):1243-1250.

O’Bryhim BE, Kung N, Van Stavern GP, et al. Association of preclinical Alzheimer disease with optical coherence tomography angiography findings. JAMA Ophthalmol. 2018;136(11):1242-1248. DOI: https://doi.org/10.1001/jamaophthalmol.2018.3556

Kwapong WR, Ye H, Peng C, et al. Retinal microvascular impairment in Parkinson’s disease and clinical correlations: an optical coherence tomography angiography study. Invest Ophthalmol Vis Sci. 2018;59(11):4399-4405. DOI: https://doi.org/10.1167/iovs.17-23230

Zou J, Liu K, Li F, et al. Retinal microvascular and geographic alterations in Parkinson’s disease as determined by optical coherence tomography angiography. PeerJ. 2020;8:e9154.

Fechter L, Baeri O, Aly L, et al. Retinal microvascular changes in multiple sclerosis and neuromyelitis optica spectrum disorder. Neurology. 2019;93(12):e1150-e1161.

De Fauw J, Ledsam JR, Romera-Paredes B, et al. Clinically applicable deep learning for diagnosis and referral in retinal disease. Nat Med. 2018;24(9):1342-1350. DOI: https://doi.org/10.1038/s41591-018-0107-6

Schmidt-Erfurth U, Bogunovic H, Sadeghipour A, et al. Machine learning to analyze optical coherence tomography data. Prog Retin Eye Res. 2018;65:40-66.

Asif S, Amjad S, Amjad M, et al. Automated diagnosis of Alzheimer’s disease using retinal optical coherence tomography and convolutional neural networks. Diagnostics (Basel). 2023;13(4):614.

Sharafeldeen A, Elnakib A, Soliman A, et al. Retinal-based deep learning framework for early detection of Alzheimer’s disease using hybrid OCT and OCTA features. Front Aging Neurosci. 2023;15:1102943.

Wisely CE, Wang D, Henao R, et al. Convolutional neural network to identify symptomatic Alzheimer’s disease using multimodal retinal imaging. Br J Ophthalmol. 2022;106(3):388-395. DOI: https://doi.org/10.1136/bjophthalmol-2020-317659

Nunes A, Silva G, Duque C, et al. Retinal texture biomarkers for the diagnosis of Parkinson’s disease using optical coherence tomography. IEEE Trans Biomed Eng. 2020;67(11):3125-3134.

Mou X, Zhao Y, Chen L, et al. Deep learning-based retinal vessel segmentation in optical coherence tomography angiography (OCTA) images: a review. Acta Ophthalmologica. 2023;101(4):365-381.

Murphy OC, Kwakyi O, Saidha S, et al. Retinal vascular dysfunction in multiple sclerosis: an optical coherence tomography angiography study. J Neurol. 2020;267(10):3041-3052.

Heisler M, Karst S, Lo J, et al. Artificial intelligence in optical coherence tomography and optical coherence tomography angiography: current applications, technical barriers, and clinical translation. Prog Retin Eye Res. 2023;95:101150.

Bustle II, Wollstein G, Schuman JS. OCT standardization and interoperability. Prog Retin Eye Res. 2014;39:39-46.

Khan SM, Liu X, Nath S, et al. Global availability of data for orofacial clefts and retinal diseases in artificial intelligence research: a systematic review. Lancet Digit Health. 2021;3(11):e713-e722.

Ting DSW, Peng L, Varadarajan AV, et al. Deep learning applications in ophthalmology with images: progress and challenges. Prog Retin Eye Res. 2019;72:100760. DOI: https://doi.org/10.1016/j.preteyeres.2019.04.003

Wagner SK, Hughes F, Cortina-Borja M, et al. AlzEye: longitudinal record-level linkage of ophthalmic imaging and hospital admissions of 353,157 patients in London, UK. BMJ Open. 2022;12(3):e058552. DOI: https://doi.org/10.1136/bmjopen-2021-058552

Lauermann JL, Treder M, Alnawaiseh M, et al. Influence of image quality and artifacts on automated quantification of retinal microvasculature using optical coherence tomography angiography. Graefes Arch Clin Exp Ophthalmol. 2019;257(12):2617-2624. DOI: https://doi.org/10.1007/s00417-019-04338-7

Qiu S, Chang GH, Panagia M, et al. Deep learning analysis of optical coherence tomography images for the detection of mild cognitive impairment and Alzheimer’s disease. Ophthalmol Sci. 2022;2(4):100196.

Additional Files

Published

2026-10-01

How to Cite

1.
Retinal Biomarkers of Neurodegeneration: The Emerging Role of Optical Coherence Tomography (OCT) and Artificial Intelligence (AI). Optom Clin y Cienc Vis [Internet]. 2026 Oct. 1 [cited 2026 Oct. 3];5(2):9. Available from: https://revistaoccv.es/index.php/occv/article/view/69

AI Declaration

No generative AI use declared

Similar Articles

1-10 of 34

You may also start an advanced similarity search for this article.