Evidence map›Paper›PMID 33967931›Full record

ArticleFrontiers in neurology2021

Computer Vision for Brain Disorders Based Primarily on Ocular Responses.

Xiaotao Li, Fangfang Fan, Xuejing Chen, Juan Li, Li Ning, Kangguang Lin, Zan Chen, Zhenyun Qin, Albert S Yeung, Xiaojian Li and 2 more

Open access · goldAbstract read
In one paragraph

Article in Frontiers in neurology, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed, 1 pooled it
1.0field-weighted citation impact, top 25% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

3 citing papers in PubMed, 1 synthesis or guideline pooled it, 9 citations in OpenAlex.

  1. Pooled it
  2. Article
  3. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

12 authors at 5 institutions in 3 countries.

Xiaotao LiBrain Cognition and Brain Disease Institute, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China.
Fangfang FanDepartment of Neurology, Harvard Medical School, Harvard University, Boston, MA, United States.
Xuejing ChenRetina Division, Department of Ophthalmology, Boston University Eye Associates, Boston University, Boston, MA, United States.
Juan LiBrain Cognition and Brain Disease Institute, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China.
Li NingCenter for High Performance Computing, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China.
Kangguang LinDepartment of Affective Disorders and Academician Workstation of Mood and Brain Sciences, The Affiliated Brain Hospital of Guangzhou Medical University (Guangzhou Huiai Hospital), Guangzhou, China.
Zan ChenBrain Cognition and Brain Disease Institute, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China.
Zhenyun QinKey Laboratory for Nonlinear Mathematical Models and Methods, School of Mathematical Science, Fudan University, Shanghai, China.
Albert S YeungDepression Clinical and Research Program, Department of Psychiatry, Massachusetts General Hospital, Boston, MA, United States.
Xiaojian LiBrain Cognition and Brain Disease Institute, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China.
Liping WangBrain Cognition and Brain Disease Institute, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China.
Kwok-Fai SoGuangdong-Hong Kong-Macau Institute of Central Nervous System (CNS) Regeneration, Jinan University, Guangzhou, China.
Chinese Academy of Sciences · CNJinan University · CNBoston University · USFudan University · CNHarvard University · US

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

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.

Indexed as

brain disorderscognitive neurosciencecomputer visioneye-brain engineeringocular assessmentretina

Identifiers

PMID33967931
PMCPMC8096911
OpenAlexW3152932796

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.