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In Alzheimer's & dementia (Amsterdam, Netherlands)

Background : Research investigating treatments and interventions for cognitive decline fail due to difficulties in accurately recognizing behavioral signatures in the presymptomatic stages of the disease. For this validation study, we took our previously constructed digital biomarker-based prognostic models and focused on generalizability and robustness of the models.

Method : We validated prognostic models characterizing subjects using digital biomarkers in a longitudinal, multi-site, 40-month prospective study collecting data in memory clinics, general practitioner offices, and home environments.

Results : Our models were able to accurately discriminate between healthy subjects and individuals at risk to progress to dementia within 3 years. The model was also able to differentiate between people with or without amyloid neuropathology and classify fast and slow cognitive decliners with a very good diagnostic performance.

Conclusion : Digital biomarker prognostic models can be a useful tool to assist large-scale population screening for the early detection of cognitive impairment and patient monitoring over time.

Buegler Maximilian, Harms Robbert, Balasa Mircea, Meier Irene B, Exarchos Themis, Rai Laura, Boyle Rory, Tort Adria, Kozori Maha, Lazarou Eutuxia, Rampini Michaela, Cavaliere Carlo, Vlamos Panagiotis, Tsolaki Magda, Babiloni Claudio, Soricelli Andrea, Frisoni Giovanni, Sanchez-Valle Raquel, Whelan Robert, Merlo-Pich Emilio, Tarnanas Ioannis


Altoida Neuro Motor Index, “Alzheimers disease”, artificial intelligence, augmented reality, cognitive aging, digital biomarker, machine learning, risk prediction