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Updated: May 1, 2026

A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment
Published on: January 11, 2020
Can a novel computerized cognitive screening test provide additional information for early detection of Alzheimer's
Ioannis Tarnanas1, Magda Tsolaki2, Tobias Nef3
1Gerontechnology and Rehabilitation Group, University of Bern, Bern, Switzerland; Clinical & Biological Psychology, Institute of Psychology and Education, University of Ulm, Ulm, Germany.
Background:
Virtual reality testing of everyday activities is a novel type of computerized assessment that measures cognitive, executive, and motor performance as a screening tool for early dementia. This study used a virtual reality day-out task (VR-DOT) environment to evaluate its predictive value in patients with mild cognitive impairment (MCI).
Methods:
One hundred thirty-four patients with MCI were selected and compared with 75 healthy control subjects. Participants received an initial assessment that included VR-DOT, a neuropsychological evaluation, magnetic resonance imaging (MRI) scan, and event-related potentials (ERPs). After 12 months, participants were assessed again with MRI, ERP, VR-DOT, and neuropsychological tests.
Results:
At the end of the study, we differentiated two subgroups of patients with MCI according to their clinical evolution from baseline to follow-up: 56 MCI progressors and 78 MCI nonprogressors. VR-DOT performance profiles correlated strongly with existing predictive biomarkers, especially the ERP and MRI biomarkers of cortical thickness.
Conclusions:
Compared with ERP, MRI, or neuropsychological tests alone, the VR-DOT could provide additional predictive information in a low-cost, computerized, and noninvasive way.
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