Biomarkers.

Background: Recent technological advancements have revolutionized our approach to healthcare, enabling us to harness the potential of smartphones and wearables to collect data that can be used to characterize Alzheimer's disease (AD) heterogeneity and to develop digital biomarkers. Our focus is to create comprehensive cross-domain digital datasets and establish an infrastructure that allows for seamless data sharing. Central to accelerating the potential of digital biomarkers for more accurate and early detection is privacy-protecting data access, which when combined with deep molecular phenotyping, will enhance our understanding of the biological mechanisms underlying clinical expression.

Biomarkers.

Background: Speech is a predominant mode of human communication. Speech digital recordings are inexpensive to record and contain rich health related information. Deep learning, a key method, excels in detecting intricate patterns, however, it requires substantial training data.

Digital phenotyping: An equal opportunity approach to reducing disparities in Alzheimer's disease and related dementia research.

A rapidly aging world population is fueling a concomitant increase in Alzheimer's disease (AD) and related dementias (ADRD). Scientific inquiry, however, has largely focused on White populations in Australia, the European Union, and North America. As such, there is an incomplete understanding of AD in other populations.