Evaluating Digital Device Technology in Alzheimer's Disease via Artificial Intelligence

The use of digital technologies may help to diagnose Alzheimers Disease (AD) at the pre-symptomatic stage. However, before implementation into clinical practice, digital measures (DMs) need to be evaluated for their diagnostic benefit compared to established questionnaire-based assessments, such as the Mini-Mental State Examination (MMSE) for cognition and Functional Activity Questionnaire (FAQ) for daily functioning. Moreover, the quantitative and qualitative relationship of DMs to these well understood scores needs to be clarified to aid interpretation. In this work we analyzed data from 148 subjects, 58 cognitively normal and 90 at different stages of the disease, which had performed a smartphone based virtual reality game to assess cognitive function. In addition, we used clinical data from Alzheimers Disease Neuroimaging Initiative (ADNI). We employed an Artificial Intelligence (AI) based approach to elucidate the relationship of DMs to questionnaire-based cognition and functional activity scores. In addition, we used Machine Learning (ML) and statistical methods to assess the diagnostic benefit of DMs compared to questionnaire-based scores. We found non-trivial relationships between DMs, MMSE, and FAQ which can be visualized as a complex network. DMs, in particular those reflecting scores of individual tasks in the virtual reality game, showed a better ability to discriminate between different stages of the disease than questionnaire-based methods. Our results indicate that DMs have the potential to act as a crucial measure in the early diagnosis and staging of AD.