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Updated: Dec 8, 2025

A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment
Published on: January 11, 2020
Dementia medical screening using mobile applications: A systematic review with a new mapping model
Fadi Thabtah1, David Peebles2, Jenny Retzler2
1Digital Technologies, Manukau Institute of Technology, Auckland, New Zealand.
Abstract:
Early detection is the key to successfully tackling dementia, a neurocognitive condition common among the elderly. Therefore, screening using technological platforms such as mobile applications (apps) may provide an important opportunity to speed up the diagnosis process and improve accessibility. Due to the lack of research into dementia diagnosis and screening tools based on mobile apps, this systematic review aims to identify the available mobile-based dementia and mild cognitive impairment (MCI) apps using specific inclusion and exclusion criteria. More importantly, we critically analyse these tools in terms of their comprehensiveness, validity, performance, and the use of artificial intelligence (AI) techniques. The research findings suggest diagnosticians in a clinical setting use dementia screening apps such as ALZ and CognitiveExams since they cover most of the domains for the diagnosis of neurocognitive disorders. Further, apps such as Cognity and ACE-Mobile have great potential as they use machine learning (ML) and AI techniques, thus improving the accuracy of the outcome and the efficiency of the screening process. Lastly, there was overlapping among the dementia screening apps in terms of activities and questions they contain therefore mapping these apps to the designated cognitive domains is a challenging task, which has been done in this research.
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