Artificial intelligence in ophthalmology: an insight into neurodegenerative disease.

Abstract

The aging world population accounts for the increasing prevalence of neurodegenerative diseases such as Alzheimer's and Parkinson's which carry a significant health and economic burden. There is therefore a need for sensitive and specific noninvasive biomarkers for early diagnosis and monitoring. Advances in retinal and optic nerve multimodal imaging as well as the development of artificial intelligence deep learning systems (AI-DLS) have heralded a number of promising advances of which ophthalmologists are at the forefront. The association among retinal vascular, nerve fiber layer, and macular findings in neurodegenerative disease is well established. In order to optimize the use of these ophthalmic parameters as biomarkers, validated AI-DLS are required to ensure clinical efficacy and reliability. Varied image acquisition methods and protocols as well as variability in neurogenerative disease diagnosis compromise the robustness of ground truths that are paramount to developing high-quality training datasets. In order to produce effective AI-DLS for the diagnosis and monitoring of neurodegenerative disease, multicenter international collaboration is required to prospectively produce large inclusive datasets, acquired through standardized methods and protocols. With a uniform approach, the efficacy of resultant clinical applications will be maximized.