Assessing the utility of automated and pen‐and‐paper cognitive assessment tools for underrepresented groups in the UK

Background Pen-and-paper cognitive assessment tools to detect dementia have higher rates of misdiagnosis amongst minority populations, especially those who complete the assessment in their second language. CognoSpeak is an automated cognitive assessment tool that uses machine learning to detect early signs of cognitive impairment from speech. We assess the utility of different pen-and-paper cognitive assessments and CognoSpeak in ethnic minority populations living in the UK.

Methods Research champions from four community centres across Yorkshire recruited cognitively healthy adults from their community: 51 Somali, 50 South Asian (South Yorkshire), 50 Chinese, and 49 South Asian (West Yorkshire). Participants completed the Montreal Cognitive Assessment (MoCA), Rowland Universal Dementia Assessment Scale (RUDAS), Multicultural Cognitive Examination (MCE), and CognoSpeak.

Results A high percentage (47.5%) of participants recruited from ethnic minority community centres were misclassified as cognitively impaired with the MoCA, compared to just 3.4% in the RUDAS and 2% in the MCE. An acoustic-based SVM model analysis of responses to CognoSpeak achieved 83% accuracy in the ethnic minority cohort, at a similar rate to monolinguals (86%). Linguistic and text-based models showed higher levels of bias.

Conclusion Cognitive assessments, such as the MCE and RUDAS, may be superior to the MoCA in multilingual ethnic minority populations. Automated AI tools like CognoSpeak show promise in reducing healthcare burden in detecting dementia; however, additional work is required on managing implicit bias in any AI model before they could be clinically implemented.