Identifying incipient dementia individuals using machine learning and amyloid imaging


Identifying individuals destined to develop Alzheimer’s dementia within time frames acceptable for clinical trials constitutes an important challenge to design studies to test emerging disease-modifying therapies. Although amyloid-β protein is the core pathological feature of Alzheimer’s disease, biomarkers of neuronal degeneration are the only ones believed to provide satisfactory predictions of clinical progression within short time frames. Here, we propose a machine learning based probabilistic method designed to assess the progression to dementia within 24 months, based on the regional information from a single amyloid Positron Emission Tomography scan.📍

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