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Updated: Oct 21, 2025

A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment
Published on: January 11, 2020
The Screening Accuracy of a Visually Based Montreal Cognitive Assessment Tool for Older Adult Hearing Aid Users
Nattawan Utoomprurkporn1,2, Joshua Stott3, Sergi G Costafreda4
1UCL Ear Institute, London, United Kingdom.
Objectives:
This research aims to validate a modified visually based Montreal Cognitive Assessment for hearing-aid users (MoCA-HA). This population should be the target of cognitive screening due to high risk of developing dementia.
Design:
Case-control study.
Setting:
The participants were recruited from referral hearing-aid center and memory clinic in central London, United Kingdom.
Participant:
75 hearing-aid users were recruited. Of these, thirty were cognitively intact controls with hearing impairment (NC-HI); thirty had mild cognitive impairment with hearing impairment (MCI-HI); fifteen had dementia with hearing impairment (D-HI).
Measurements:
The baseline characteristics and analysis of the MoCA-HA for the NC-HI were recorded. The MoCA-HA performance of the MCI-HI cohort and D-HI cohort were also studied.
Results:
The cutpoint of <26 yields 93.3% sensitivity with 80% specificity in distinguishing MCI-HI from NC-HI. The specificity increased to 95.6% in screening for all cognitive impairment (MCI-HI and D-HI) from NC-HI.
Conclusion:
The MoCA-HA has been validated with a cutpoint which is comparable to the traditional MoCA. This tool may help clinicians to early identify older adult hearing-aid users for appropriate cognitive evaluation.

