ArticleComputational intelligence and neuroscience2022
Exploratory Research on Key Technology of Human-Computer Interactive 2.5-Minute Fast Digital Early Warning for Mild Cognitive Impairment.
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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Who cites it
5 citing papers in PubMed.
- A novel early screening approach for MCI due to AD based on a "maze" hand-interaction kinetic paradigm.Frontiers in neuroscience · 2026Article
- Digital Health Technologies Applied in Patients With Early Cognitive Change: Scoping Review.Journal of medical Internet research · 2025Article
- A study on the exploration of mild cognitive impairment in Parkinson's disease based on decision-making cognitive computing.Frontiers in neuroscience · 2024Article
- A New Smart 2-Min Mobile Alerting Method for Mild Cognitive Impairment Due to Alzheimer's Disease in the Community.Brain sciences · 2023Article
- Vestibular cognition assessment system: Tablet-based computerized visuospatial abilities test battery.Frontiers in psychology · 2023Article
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Authors and funding
9 authors.
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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.
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