Digital Biomarkers for the Early Detection of Mild Cognitive Impairment: Artificial Intelligence Meets Virtual Reality - Cavedoni, Silvia; Chirico, Alice; Pedroli, Elisa; Cipresso, Pietro; Riva, Giuseppe (Frontiers in Human Neuroscience, 2020)
Elderlies affected by Mild Cognitive Impairment (MCI) usually refer a perceived decline in cognitive functions, which deeply impacts on their quality of life. This subtle waning, despite it cannot be diagnosable as dementia, is noted by caregivers starting from their relatives’ behaviors. Crucially, if this condition is not detected on time also 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 ecological controlled simulations resembling patients’ daily life contexts in which their 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 creating a predictive model to identify specific patterns of cognitive and motor impairment in MCI. Therefore, this new dimensional cognitive-behavioral assessment would reveal elderlies' neural alterations and impaired cognitive functions, typical of MCI and dementia, even in early stages for more time sensitive interventions.