Retinal imaging and Alzheimer's disease: a future powered by Artificial Intelligence
Hamidreza Ashayeri1, Ali Jafarizadeh2, Milad Yousefi3
1Neuroscience Research Center (NSRC), Tabriz University of Medical Sciences, Tabriz, Iran.
Summary
Artificial Intelligence (AI) analyzes retinal images to detect Alzheimer's disease (AD) changes, aiding in early prediction and diagnosis. This technology shows promise for identifying at-risk individuals and assessing disease prognosis.
Area of Science:
- Ophthalmology
- Neurology
- Artificial Intelligence
Background:
- Alzheimer's disease (AD) is a neurodegenerative disorder impacting brain tissue.
- Shared embryonic origin between the retina and brain leads to visual deficits in AD patients.
- Retinal changes, including thickness and vessel density alterations, can precede clinical AD symptoms.
Purpose of the Study:
- To review the application of Artificial Intelligence (AI) in analyzing retinal images for Alzheimer's disease (AD) prediction, diagnosis, and prognosis.
- To discuss the potential of AI in detecting subtle retinal changes associated with AD.
- To explore the development of AI algorithms and datasets for AD detection using retinal imaging.
Main Methods:
- Review of existing research on AI and retinal imaging in Alzheimer's disease.
- Analysis of AI algorithms trained on retinal images to identify AD hallmarks.
- Utilizing machine vision to detect changes in retinal thickness and vessel density.
Main Results:
- AI demonstrates high accuracy, sensitivity, and specificity in classifying retinal images between AD patients and healthy individuals.
- AI can identify individuals with a family history of AD based on retinal image characteristics.
- Retinal OCTA Segmentation (ROSE) dataset development facilitates AI research in AD.
Conclusions:
- AI holds significant potential for the prediction, diagnosis, and prognosis of Alzheimer's disease using retinal imaging.
- AI-driven analysis of retinal changes offers a non-invasive approach to AD detection.
- Further cohort studies are needed to validate AI's role in monitoring at-risk individuals and assessing AD prognosis.
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