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

The use of digital technologies may help to diagnose Alzheimer's 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 Mini-Mental State Examination (MMSE) and Functional Activity Questionnaire (FAQ). We analyzed data from smartphone based virtual reality game and Alzheimer's Disease Neuroimaging Initiative (ADNI). We employed an Artificial Intelligence (AI) based approach to elucidate the relationship of DMs to MMSE and FAQ. Furthermore, 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. DM 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.