Here we summarize abundant excuses that support the eye detection can be as a noninvasive approach to early diagnose some brain disorders, including Alzheimer disease (AD), autism spectrum disorder (ASD), schizophrenia (SZ)  and major depressive disorder (MDD). Real-time ocular responses in everyone are tightly associated with emotional state and cognitive processing in the brain. At the eyeball areas, at least changes involved saccades, pupil response, blinking, microvascular network morphology and thickness of retinal nerve fiber layer (RNFL) are potentially to be candidates of biomarker to screen and evaluate brain disorders. These days medical big data will be more accessible especially with the help of advanced artificial intelligence (AI) to analyze precisely. Computer vision combined with machine-learning based AIparticularly deep-learning neural networks sheds a new light on  cognitive neuroscience, and many patients with psychotic disorders will primarily benefit from this interdisciplinary advance to induce self-evaluation and real-time intervention on brain disorders. Finally, we call for more AI applications going to approach brain disorder issues, meanwhile, AI technology used in brain healthcare should be working together with biological intelligence as we are running an AI manifestation for major depression .
The present review presents multiple techniques in which ocular assessments may serve as a noninvasive approach for the early diagnoses of various cognitive and psychiatric disorders, such as Alzheimer's disease (AD), autism spectrum disorder (ASD), schizophrenia (SZ) , and major depressive disorder (MDD). Real-time ocular responses are tightly associated with emotional and cognitive processing within the central nervous system. Patterns seen in saccades, pupillary responses, and 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. Additionally, rapid advances in artificial intelligence (AI) present a growing opportunity to use machine-learning-based AI, especially deep-learning neural networks , to shed new light on the field of cognitive neuroscience, which may lead to novel evaluations and interventions via ocular approaches for cognitive and psychiatric disorders .
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Here we summarize abundant excuses that support the eye detection can be
The present review presents multiple techniques in which ocular assessments may serve
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to early diagnose some brain disorders, including Alzheimer
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in the brain. At the eyeball areas, at least changes involved saccades, pupil response, blinking, microvascular network morphology and thickness of retinal nerve fiber layer (RNFL) are potentially to be candidates of biomarker to screen and evaluate brain disorders. These days medical big data will be more accessible especially with the help of advanced
within the central nervous system. Patterns seen in saccades, pupillary responses, and 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. Additionally, rapid advances in
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and many patients with psychotic disorders will primarily benefit from this interdisciplinary advance to induce self-evaluation and real-time intervention on brain disorders. Finally, we call for more AI applications going to approach brain disorder issues, meanwhile, AI technology used in brain healthcare should be working together with biological intelligence as we are running an AI manifestation for major depression
which may lead to novel evaluations and interventions via ocular approaches for cognitive and psychiatric disorders