Machine Learning for Noninvasive Diagnosis of Neurodegenerative Diseases Using Retinal and Optic Nerve Imaging: A Comprehensive Review.
Machine Learning for Noninvasive Diagnosis of Neurodegenerative Diseases Using Retinal and Optic Nerve Imaging: A Comprehensive Review.
Where did the research take place?
The study site has not been established. Author addresses may differ from where the research occurred.
Isfahan, IR · Author affiliation
Medical Image and Signal Processing Research Center, Isfahan University of Medical Sciences, Isfahan, Iran.Location evidence
Durham, GB · Author affiliation
Department of Computer Science, Durham University, Durham, United Kingdom.Location evidence
Newcastle upon Tyne, GB · Author affiliation
Newcastle Eye Centre, Royal Victoria Infirmary, Newcastle Upon Tyne, United Kingdom.Location evidence
Tehran, IR · Author affiliation
Farabi Eye Hospital, Tehran University of Medical Science, Tehran, Iran.Location evidence
Berlin, DE · Author affiliation
Experimental and Clinical Research Center, Max Delbrueck Center for Molecular Medicine and Charité - Universitaetsmedizin Berlin, Berlin, Germany.Location evidence
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Original abstract
Neurodegenerative disorders, including Alzheimer's disease, Parkinson's disease, and multiple sclerosis, encompass a wide range of chronic conditions with irreversible damage to the central nervous system. Current diagnostic workups of these disorders rely on invasive, time-consuming, and costly tests, such as magnetic resonance imaging and cerebrospinal fluid analysis, preventing accurate decision-making and timely therapeutic interventions. The retina is an extension of the central nervous system; thus, retinal imaging, which is a noninvasive and easily accessible tool, provides a unique window to study brain pathologies. There is a great body of evidence suggesting that neurodegenerative disorders are associated with various structural and vascular problems within the retina. Notably, training machine learning models with retinal images has yielded high levels of accuracy in classifying neurodegenerative diseases, encouraging a new era for early and automated diagnosis of these disorders. This article reviews studies that use such models for classifying these disorders.