Validity and Cultural Generalisability of a 5-Minute AI-Based, Computerised Cognitive Assessment in Mild Cognitive Impairment and Alzheimer's Dementia

Abstract

<b>Introduction:</b> Early detection and monitoring of mild cognitive impairment (MCI) and Alzheimer's Disease (AD) patients are key to tackling dementia and providing benefits to patients, caregivers, healthcare providers and society. We developed the Integrated Cognitive Assessment (ICA); a 5-min, language independent computerised cognitive test that employs an Artificial Intelligence (AI) model to improve its accuracy in detecting cognitive impairment. In this study, we aimed to evaluate the generalisability of the ICA in detecting cognitive impairment in MCI and mild AD patients. <b>Methods:</b> We studied the ICA in 230 participants. 95 healthy volunteers, 80 MCI, and 55 mild AD participants completed the ICA, Montreal Cognitive Assessment (MoCA) and Addenbrooke's Cognitive Examination (ACE) cognitive tests. <b>Results:</b> The ICA demonstrated convergent validity with MoCA (Pearson r=0.58, p<0.0001) and ACE (r=0.62, p<0.0001). The ICA AI model was able to detect cognitive impairment with an AUC of 81% for MCI patients, and 88% for mild AD patients. The AI model demonstrated improved performance with increased training data and showed generalisability in performance from one population to another. The ICA correlation of 0.17 (<i>p</i> = 0.01) with education years is considerably smaller than that of MoCA (<i>r</i> = 0.34, <i>p</i> < 0.0001) and ACE (<i>r</i> = 0.41, <i>p</i> < 0.0001) which displayed significant correlations. In a separate study the ICA demonstrated no significant practise effect over the duration of the study. <b>Discussion:</b> The ICA can support clinicians by aiding accurate diagnosis of MCI and AD and is appropriate for large-scale screening of cognitive impairment. The ICA is unbiased by differences in language, culture, and education.

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