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Updated: May 26, 2025

A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment
Published on: January 11, 2020
Prospective observational study to evaluate the feasibility of the mobile app for mild cognitive impairment detection
Reo Hamaguchi1, Seiji Hongo2, Naoto Doi3
1Department of Digital Therapeutics, Graduate School of Medicine, Juntendo University, Tokyo, Japan.
Introduction:
The increasing prevalence of dementia in aging populations necessitates effective and accessible cognitive screening tools. This study aimed to evaluate the feasibility and reliability of a newly developed mobile app for detecting and screening mild cognitive impairment (MCI).
Methods:
The mobile app, developed by LifeQuest Co., Ltd. (Minato-ku, Tokyo), is an original tool inspired by the Japanese version of the Montreal Cognitive Assessment (MoCA-J). A prospective observational study was conducted with 20 participants, including healthy individuals, MCI patients, and those with mild to moderate-severe dementia. Participants completed both the mobile app and the MoCA-J in a randomized order within a two-week period, with a minimum one-day interval between tests.
Results And Conclusion:
The intraclass correlation coefficient (ICC) between the mobile app and the MoCA-J was 0.956 (95% CI: 0.89-0.983), demonstrating a very high level of correlation. All participants successfully completed the mobile app assessment, highlighting its feasibility across various cognitive levels. Although minor technical issues and usability challenges were identified, the results support the mobile app as a reliable and user-friendly alternative for cognitive screening. Further studies with larger sample sizes are necessary to validate these findings and refine the app for broader clinical use.
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