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

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.

Artificial intelligence as an aid to diagnosing dementia: an overview

“…Throughout this period, different metrics are collected from the touchscreen itself, as well as measurements taken from a gyroscope and accelerometers to assess patient functioning. Analysis of data results in this study revealed certain neuro‐parameters ( ie perceptual motor function) that correlate with subdomains currently tested on validated neuropsychological assessments 39,40 …”

“…Early detection of MCI based on behavioural analysis includes technological approaches, including ML, virtual reality (VR) and gait kinematic analysis, which are currently being explored as part of a new conventional assessment. The outcome of this would be a continuous and time‐sensitive assessment, which would be in line with new DSM‐V guidelines 40,41 …”

“…Kinematic analysis uses technology to record a patient's movements automatically within a controlled setting, often during iADL assessments 45 . It is thought to add value when assessing cognition with gait kinematics strongly relating to cognitive decline through association with neurophysiological changes and brain volume changes in certain regions 40 …”

“…This is particularly important with a recognised potential threefold difference in conversion rate from MCI to dementia between recruits from community and memory clinics 37,38 . The growth of this approach could offer patients and caregivers an optimised quality of life and treatment plan 40 …”

Machine Learning to Predict Brain Amyloid Pathology in Pre-dementia Alzheimer’s Disease Using QEEG Features and Genetic Algorithm Heuristic

“… Buegler et al (2020) built digital biomarker-based prognostic models which predicted the risk to progress to dementia within 3 years. Those digital biomarker-based models are reviewed by Cavedoni et al (2020) .…”