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Retinal imaging and Alzheimer’s disease: a future powered by Artificial Intelligence

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Abstract

Alzheimer’s disease (AD) is a neurodegenerative condition that primarily affects brain tissue. Because the retina and brain share the same embryonic origin, visual deficits have been reported in AD patients. Artificial Intelligence (AI) has recently received a lot of attention due to its immense power to process and detect image hallmarks and make clinical decisions (like diagnosis) based on images. Since retinal changes have been reported in AD patients, AI is being proposed to process images to predict, diagnose, and prognosis AD. As a result, the purpose of this review was to discuss the use of AI trained on retinal images of AD patients. According to previous research, AD patients experience retinal thickness and retinal vessel density changes, which can occasionally occur before the onset of the disease’s clinical symptoms. AI and machine vision can detect and use these changes in the domains of disease prediction, diagnosis, and prognosis. As a result, not only have unique algorithms been developed for this condition, but also databases such as the Retinal OCTA Segmentation dataset (ROSE) have been constructed for this purpose. The achievement of high accuracy, sensitivity, and specificity in the classification of retinal images between AD and healthy groups is one of the major breakthroughs in using AI based on retinal images for AD. It is fascinating that researchers could pinpoint individuals with a positive family history of AD based on the properties of their eyes. In conclusion, the growing application of AI in medicine promises its future position in processing different aspects of patients with AD, but we need cohort studies to determine whether it can help to follow up with healthy persons at risk of AD for a quicker diagnosis or assess the prognosis of patients with AD.

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Data availability

The entirety of the data produced or examined throughout this study has been incorporated into this manuscript.

Abbreviations

Aβ-42:

Amyloid beta-42

AD:

Alzheimer’s disease

ADNI:

Alzheimer’s disease neuroimaging initiative

ADSA:

Alzheimer’s disease assessment scale

AI:

Artificial Intelligence

AUC:

Area under the curve

CERAD:

Consortium to establish a registry for Alzheimer`s disease

CNN:

Convolutional neural network

CSF:

Cerebrospinal fluid

DL:

Deep learning

FDA:

Food and Drug Administration

GCL:

Ganglion cell layer

INL:

Inner nuclear layer

IPL:

Inner plexiform layer

HC:

Healthy controls

MCI:

Mild cognitive impairment

ML:

Machine learning

MMSE:

Mini-mental State Examination

MRI:

Magnetic resonance imaging

OCT:

Optical coherence tomography

OCTA:

Optical coherence tomography angiopathy

ONL:

Outer nuclear layer

OPL:

Outer plexiform layer

PD:

Parkinson’s disease

PET:

Positron emission tomography

RNFL:

Retina nerve fiber layer

ROSE:

Retinal octa segmentation dataset

SLO:

Scanning laser ophthalmoscope

SVM:

Support vector machine

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Authors and Affiliations

  1. Neuroscience Research Center (NSRC), Tabriz University of Medical Sciences, Tabriz, Iran

    Hamidreza Ashayeri

  2. Nikookari Eye Center, Tabriz University of Medical Sciences, Tabriz, Iran

    Ali Jafarizadeh & Fereshteh Farhadi

  3. Faculty of Mathematics, Statistics and Computer Sciences, University of Tabriz, Tabriz, Iran

    Milad Yousefi

  4. Department of Ophthalmology, Nikookari Eye Center, Tabriz University of Medical Sciences, Tabriz, Iran

    Alireza Javadzadeh

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AJav and AJaf conceptualized the manuscript. HA and AJaf conducted a literature review and drafted the manuscript. AJav, MY, and FF provided comments and revised the manuscript. MY created and designed all figures. All authors read and approved the final version of the manuscript.

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Correspondence to Alireza Javadzadeh.

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Ashayeri, H., Jafarizadeh, A., Yousefi, M. et al. Retinal imaging and Alzheimer’s disease: a future powered by Artificial Intelligence. Graefes Arch Clin Exp Ophthalmol (2024). https://doi.org/10.1007/s00417-024-06394-0

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