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Updated: Aug 3, 2025
A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment
Published on: January 11, 2020
Use of artificial intelligence techniques for detection of mild cognitive impairment: A systematic scoping review
Li JuanVivian Quek1, Maria Rosaliini Heikkonen1, Ying Lau1
1Alice Lee Centre for Nursing Studies, Yong Loo Lin School of Medicine, National University of Singapore, Singapore city, Singapore.
Aims And Objectives:
The objective of this scoping review is to explore the types and mechanisms of Artificial intelligence (AI) techniques for detecting mild cognitive impairment (MCI).
Background:
Early detection of MCI is crucial because it may progress to Alzheimer's disease.
Design:
A systematic scoping review.
Methods:
Five-step framework of Arksey and O'Malley was used following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for scoping reviews checklist. A total of 11 databases (PubMed, EMBASE, CINAHL, Cochrane Library, Scopus, Web of Science, IEEE Explore, Science.gov, ACM digital library, arXIV and ProQuest) was used to search from inception till 17th December 2021. Grey literature and reference list were searched. Articles screening and data charting were conducted by two independent reviewers.
Results:
There were a total of 70 articles included from 2011 to 2022 across 16 countries. Four types of AI techniques were found, namely machine learning (ML), deep learning (DL), fuzzy logic (FL) and technique combinations. Herein, ML detects similar pattern within preselected data to classify subjects into non-MCI or MCI groups. Meanwhile, DL performs classification based on data patterns and data analyses are performed by themselves. Furthermore, FL utilises human-defined rules to decide the degree to which a person has MCI. A combination of AI techniques enhances the feature preparation phase for ML or DL to perform accurate classification.
Conclusion:
Although AI-based MCI detection tool is critical for healthcare decision-making, clinical utility and risks remain underexplored. Hopefully, this review equips clinicians with background AI knowledge to address these clinical concerns. Hence, future research should explore more techniques and representative datasets to improve AI development.
Relevance To Clinical Practice:
Results of this review can increase the knowledge of AI-based MCI detection tools.
Review Registration:
This study protocol was registered in the Open Science Framework Registries (https://osf.io/45rdt).
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