Li Xiaotao, Fan Fangfang, Chen Xuejing, Li Juan, Ning Li, Lin Kangguang, Chen Zan, Qin Zhenyun, Yeung Albert S, Li Xiaojian, Wang Liping, So Kwok-Fai
Brain Cognition and Brain Disease Institute, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China.
Shenzhen-Hong Kong Institute of Brain Science-Shenzhen Fundamental Research Institutions, Shenzhen, China.
Front Neurol. 2021 Apr 21;12:584270. doi: 10.3389/fneur.2021.584270. eCollection 2021.
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.
实时眼部反应与中枢神经系统内的情绪和认知处理密切相关。在扫视、瞳孔反应和自发眨眼以及通过门诊眼科成像可视化的视网膜微血管结构和形态中观察到的模式，是用于筛查和评估认知及精神疾病的潜在生物标志物。在本综述中，我们概述了多种技术，通过这些技术眼部评估可作为一种非侵入性方法，用于早期检测各种脑部疾病，如自闭症谱系障碍（ASD）、阿尔茨海默病（AD）、精神分裂症（SZ）和重度抑郁症（MDD）。此外，人工智能（AI）的快速发展为使用基于机器学习的AI，尤其是结合深度学习神经网络的计算机视觉（CV），来为认知神经科学领域带来新的见解提供了越来越多的机会，这很可能会带来针对脑部疾病的新评估和干预措施。因此，我们强调主要基于眼部特征使用AI评估脑部疾病的潜力。
