Artificial Intelligence in Eye Movements Analysis for Alzheimers Disease Early Diagnosis
- Authors: Farabi Maleki S.1, Yousefi M.2, Sobhi N.1, Jafarizadeh A.1, Alizadehsani R.3, Gorriz-Saez J.M.4
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Affiliations:
- Nikookari Eye Center, Tabriz University of Medical Sciences
- Faculty of Mathematics, Statistics, and Computer Sciences, University of Tabriz
- Institute for Intelligent Systems Research and Innovation, Deakin University
- Data Science and Computational Intelligence Institute, Universidad de Granada
- Issue: Vol 21, No 3 (2024)
- Pages: 155-165
- Section: Medicine
- URL: https://ruspoj.com/1567-2050/article/view/643746
- DOI: https://doi.org/10.2174/0115672050322607240529075641
- ID: 643746
Cite item
Full Text
Abstract
:As the world's population ages, Alzheimer's disease is currently the seventh most common cause of death globally; the burden is anticipated to increase, especially among middle-class and elderly persons. Artificial intelligence-based algorithms that work well in hospital environments can be used to identify Alzheimer's disease. A number of databases were searched for English-language articles published up until March 1, 2024, that examined the relationships between artificial intelligence techniques, eye movements, and Alzheimer's disease. A novel non-invasive method called eye movement analysis may be able to reflect cognitive processes and identify anomalies in Alzheimer's disease. Artificial intelligence, particularly deep learning, and machine learning, is required to enhance Alzheimer's disease detection using eye movement data. One sort of deep learning technique that shows promise is convolutional neural networks, which need further data for precise classification. Nonetheless, machine learning models showed a high degree of accuracy in this context. Artificial intelligence-driven eye movement analysis holds promise for enhancing clinical evaluations, enabling tailored treatment, and fostering the development of early and precise Alzheimer's disease diagnosis. A combination of artificial intelligence-based systems and eye movement analysis can provide a window for early and non-invasive diagnosis of Alzheimer's disease. Despite ongoing difficulties with early Alzheimer's disease detection, this presents a novel strategy that may have consequences for clinical evaluations and customized medication to improve early and accurate diagnosis.
About the authors
Shadi Farabi Maleki
Nikookari Eye Center, Tabriz University of Medical Sciences
Email: info@benthamscience.net
Milad Yousefi
Faculty of Mathematics, Statistics, and Computer Sciences, University of Tabriz
Email: info@benthamscience.net
Navid Sobhi
Nikookari Eye Center, Tabriz University of Medical Sciences
Email: info@benthamscience.net
Ali Jafarizadeh
Nikookari Eye Center, Tabriz University of Medical Sciences
Author for correspondence.
Email: info@benthamscience.net
Roohallah Alizadehsani
Institute for Intelligent Systems Research and Innovation, Deakin University
Email: info@benthamscience.net
Juan Manuel Gorriz-Saez
Data Science and Computational Intelligence Institute, Universidad de Granada
Email: info@benthamscience.net
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