Evaluating digital device technology in Alzheimer's disease via artificial intelligence. (31st December 2021)

• Record Type: Journal Article
• Authors: Sood, Meemansa; Harms, Robbert; Bügler, Maximilian; Tarnanas, Ioannis; Coello, Neva; Fröhlich, Holger
• Is Part Of: Alzheimer's & dementia. Volume 17(2021)Supplement 6
• Page Start: n/a
• Page End: n/a
• Publication Date: 2021-12-31
• Subjects: Alzheimer's disease -- Periodicals; Alzheimer Disease -- Periodicals; Dementia -- Periodicals; Démence; Maladie d'Alzheimer; Périodique électronique (Descripteur de forme); Ressource Internet (Descripteur de forme); 616.83
• DOI: 10.1002/alz.053574
• Languages: English
• ISSNs: 1552-5260

Abstract

Abstract: Background: Digital Measures (DMs) derived from mobile devices and smartphone applications have received a strongly increasing attention during the last years, because they could allow for an accurate, quantitative monitoring of disease symptoms, even outside clinics. In addition, DMs may help to diagnose Alzheimer's Disease (AD) in a pre‐symptomatic stage and thus increase the success chances of therapeutic interventions. However, before any use in clinical routine, DMs have to be evaluated carefully by assessing their relationship to established clinical scores and understanding their diagnostic benefit. In this regard the IMI project RADAR‐AD (www.radar‐ad.org ) has the ambition to evaluate a broad panel of digital technologies with respect to their potential for early disease diagnosis while focusing on functional activities of daily living. Method: An example of a panel of digital technology RADAR‐AD uses is a smartphone based virtual reality game resulting into an assessment of cognitive impairment. In our work we analyzed connections between digital readouts and cognitive features like MMSE (Mini Mental State Examination) via one of our recently developed Artificial Intelligence (AI) approaches called Variational Autoencoder Modular Bayesian Networks (VAMBN). Going one step further we also tested the possibility to accurately predict MMSE scores from DMs and vice versa via machine learning. Based on this finding we then simulated DMs within the ADNI cohort and re‐ran VAMBN. Result: Application of VAMBN on the data from virtual reality game resulted into a network comprising DMs, MMSE sub‐item scores and demographic features (Figure 1). It thus allowed to disentangle and quantify the relationship between DMs and established clinical scores. The simulation of DM's and application of VAMBN in the ADNI cohort allowed us to further predict connections of DMs with FAQ (Functional Activity Questionnaire) and even molecular mechanisms. Conclusion: Our results indicate that there is a significant dependency between digital readouts and clinical scores such as MMSE and FAQ. Therefore, DM's may have the potential to act as a vital measure in the diagnosis of AD in a pre‐symptomatic stage. Our next steps will focus on evaluating the diagnostic benefit of DMs compared to the questionnaire‐based FAQ.