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. 2020 Jul 24:14:245.
doi: 10.3389/fnhum.2020.00245. eCollection 2020.

Digital Biomarkers for the Early Detection of Mild Cognitive Impairment: Artificial Intelligence Meets Virtual Reality

Silvia Cavedoni  1 , Alice Chirico  2 , Elisa Pedroli  1   3 , Pietro Cipresso  1   2 , Giuseppe Riva  1   2
Affiliations

Digital Biomarkers for the Early Detection of Mild Cognitive Impairment: Artificial Intelligence Meets Virtual Reality

Silvia Cavedoni et al. Front Hum Neurosci. .

Abstract

Elderly people affected by Mild Cognitive Impairment (MCI) usually report a perceived decline in cognitive functions that deeply impacts their quality of life. This subtle waning, although it cannot be diagnosable as dementia, is noted by caregivers on the basis of their relative's behaviors. Crucially, if this condition is also not detected in time by clinicians, it can easily turn into dementia. Thus, early detection of MCI is strongly needed. Classical neuropsychological measures - underlying a categorical model of diagnosis - could be integrated with a dimensional assessment approach involving Virtual Reality (VR) and Artificial Intelligence (AI). VR can be used to create highly ecologically controlled simulations resembling the daily life contexts in which patients' daily instrumental activities (IADL) usually take place. Clinicians can record patients' kinematics, particularly gait, while performing IADL (Digital Biomarkers). Then, Artificial Intelligence employs Machine Learning (ML) to analyze them in combination with clinical and neuropsychological data. This integrated computational approach would enable the creation of a predictive model to identify specific patterns of cognitive and motor impairment in MCI. Therefore, this new dimensional cognitive-behavioral assessment would reveal elderly people's neural alterations and impaired cognitive functions, typical of MCI and dementia, even in early stages for more time-sensitive interventions.

Keywords: Artificial Intelligence; Machine Learning; Mild Cognitive Impairment; Virtual Reality; digital biomarkers; elderly; gait analysis; kinematic.

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Figures

FIGURE 1
FIGURE 1
Subject’s kinematic measures (gait) detected within a VR environment (CAVE) while performing instrumental activities of daily life (IADL) in order to refine MCI assessment by following a dimensional approach.
FIGURE 2
FIGURE 2
Schematic illustration of the innovative model proposed.
See this image and copyright information in PMC

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