Altoida, a frontrunner in harnessing augmented reality and machine learning for neurological conditions, recently unveiled groundbreaking results from the RADAR-AD consortium study. This study, featured in Nature Digital Medicine, delved into Altoida’s augmented reality (AR) and machine learning (ML)-based digital cognitive assessment, striving for early identification of Alzheimer’s disease (AD).
The ‘Remote Assessment of Disease and Relapse – Alzheimer’s Disease’ (RADAR-AD) study embarked on an independent validation journey involving 121 participants over 50. Utilizing various remote monitoring technologies (RMTs), including Altoida’s AR digital cognitive and functional assessment, the study aimed to detect cognitive and functional decline in AD. Unlike traditional clinical assessments, RMTs offer objective and frequent monitoring of daily activities, aiding early impairment detection without clinician intervention. The Altoida AR app, comprising motor and AR tasks, underwent evaluation in amyloid beta-negative healthy controls (HC), amyloid beta-positive cognitively normal preclinical AD participants (preAD), and prodromal AD participants (proAD). These tasks involved exercises testing motor skills and AR simulations of complex daily activities. Altoida’s ML model, fueled by data from internal sensors, aimed to differentiate normal from impaired participants.
This pioneering assessment not only distinguished healthy controls from preclinical and prodromal AD participants in clinical settings but also replicated success in at-home tests. The most significant revelation? Altoida’s AR app achieved performance levels in preclinical AD surpassing standard cognitive tests, without exhibiting any learning effects.
AR-based assessments, simulating instrumental daily activities, show promise in measuring early AD-related cognitive and functional changes, both in clinics and homes. RMTs, such as smartphone apps and smartwatches, with their sensitivity and objective, long-term monitoring potential, offer a window into the subtle cognitive decline in early AD stages.
RADAR-AD, an initiative funded by the Innovative Medicines Initiative (IMI), delved into AD’s impact on functioning and explored technology’s role in measuring these changes. Leveraging widely-used tech like smartphones and sensors, RADAR-AD aimed to gauge functional alterations.
Altoida’s investigational algorithm, the digital neuro signature (DNS) for mild cognitive impairment, blends motor and AR tasks. Utilizing a touch screen, accelerometer, and gyroscope data, it generates an ordinal score indicating cognitive performance probability. This machine learning model, trained on clinically validated cohorts, represents a pivotal step in identifying cognitive impairment.
