Computer Vision for Brain Disorders Based Primarily on Ocular Responses
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fneur-12-584270.pdf
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856.31 KB
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Checksum (MD5)
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Author(s)
Li, Xiaotao
Fan, Fangfang
Chen, Xuejing
Li, Juan
Ning, Li
Lin, Kangguang
Chen, Zan
Qin, Zhenyun
Yeung, Albert S.
Li, Xiaojian
Date Issued
April 2021
Journal
Frontiers in Neurology
Publisher
Frontiers Media SA
Citation
Li, Xiaotao et al. "Computer Vision for Brain Disorders Based Primarily on Ocular Responses." Frontiers in Neurology 12 (April 2021): 584270. © 2021 Li et al.
Version
Final published version
Abstract
Real-time ocular responses are tightly associated with emotional and cognitive processing within the central nervous system. Patterns seen in saccades, pupillary responses, and spontaneous blinking, as well as retinal microvasculature and morphology visualized via office-based ophthalmic imaging, are potential biomarkers for the screening and evaluation of cognitive and psychiatric disorders. In this review, we outline multiple techniques in which ocular assessments may serve as a non-invasive approach for the early detections of various brain disorders, such as autism spectrum disorder (ASD), Alzheimer's disease (AD), schizophrenia (SZ), and major depressive disorder (MDD). In addition, rapid advances in artificial intelligence (AI) present a growing opportunity to use machine learning-based AI, especially computer vision (CV) with deep-learning neural networks, to shed new light on the field of cognitive neuroscience, which is most likely to lead to novel evaluations and interventions for brain disorders. Hence, we highlight the potential of using AI to evaluate brain disorders based primarily on ocular features.
MIT Department
Massachusetts Institute of Technology. Department of Brain and Cognitive Sciences
Terms of Use
Creative Commons Attribution 4.0 International license
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.3389/fneur.2021.584270
