Evidence map›Paper›PMID 35392035›Full record

ArticleComputational intelligence and neuroscience2022

Exploratory Research on Key Technology of Human-Computer Interactive 2.5-Minute Fast Digital Early Warning for Mild Cognitive Impairment.

Nan Li, Xiaotong Yang, Wencai Du, Atsushi Ogihara, Siyu Zhou, Xiaowen Ma, Yujia Wang, Shuwu Li, Kai Li

Abstract read
In one paragraph

Article in Computational intelligence and neuroscience, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.

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0cells of the map it votes in
5citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

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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

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3 · Its place in the literature

Who cites it

5 citing papers in PubMed.

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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

9 authors.

Nan LiSchool of Humanities and Management, Zhejiang Chinese Medical University, Hangzhou 310053, China.
Xiaotong YangSchool of Medical Technology and Information Engineering, Zhejiang Chinese Medical University, Hangzhou 310053, China.
Wencai DuUniversity of Saint Joseph, Estrada Marginal da Ilha Verde 14-17, Macau, China.
Atsushi OgiharaDepartment of Health Sciences and Social Welfare, Faculty of Human Sciences, Waseda University, Tokorozawa, Japan.
Siyu ZhouSchool of Public Health, Hangzhou Normal University, Hangzhou, China.
Xiaowen MaSchool of Medical Technology and Information Engineering, Zhejiang Chinese Medical University, Hangzhou 310053, China.
Yujia WangSchool of Medical Technology and Information Engineering, Zhejiang Chinese Medical University, Hangzhou 310053, China.
Shuwu LiKaibo Medical Equipment (Hangzhou) Co., Ltd., Hangzhou 310052, China.
Kai LiSchool of Medical Technology and Information Engineering, Zhejiang Chinese Medical University, Hangzhou 310053, China.ORCID https://orcid.org/0000-0003-3519-8814

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Objective: As the preclinical stage of Alzheimer's disease (AD), Mild Cognitive Impairment (MCI) is characterized by hidden onset, which is difficult to detect early. Traditional neuropsychological scales are main tools used for assessing MCI. However, due to its strong subjectivity and the influence of many factors such as subjects' educational background, language and hearing ability, and time cost, its accuracy as the standard of early screening is low. Therefore, the purpose of this paper is to propose a new key technology of fast digital early warning for MCI based on eye movement objective data analysis. Results: Through the small sample test of human-computer interactive 2.5 fast digital early warning paradigm for MCI conducted by 32 elderly people aged 70-90 in a medical institution in Hangzhou, the two indexes of "correlation degree" and "drift rate" with statistical differences are selected. The experiment results show that AUC of this MCI early warning paradigm is 0.824. Conclusion: The key technology of human-computer interactive 2.5 fast digital early warning for MCI proposed in this paper overcomes the limitations of the existing MCI early warning tools, such as low objectification level, high dependence on professional doctors, long test time, requiring high educational level, and so on. The experiment results show that the early warning technology, as a new generation of objective and effective digital early warning tool, can realize 2.5-minute fast and high-precision preliminary screening and early warning for MCI in the elderly.

Indexed as

Alzheimer DiseaseCognitive DysfunctionAgedComputersHumansNeuropsychological TestsTechnology

Identifiers

PMID35392035
PMCPMC8983217

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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.