Alzheimer’s Screening Takes a Leap with Japan’s Machine Learning Model
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In a groundbreaking development, Japan-based Oita University and pharmaceutical company Eisai Co. have unveiled the world’s inaugural machine learning model designed to predict the accumulation of amyloid beta (Aβ) in the brain, a pivotal pathological factor in Alzheimer’s disease (AD). This revolutionary model leverages data collected from wristband sensors to easily screen brain Aβ accumulation using biological and lifestyle data. A solution to early detection challenges Alzheimer’s disease, which accounts for over 60% of dementia cases, is marked by the gradual accumulation of Aβ in the brain. This process commences approximately two decades before the onset of clinical symptoms. As a response to this pressing issue, there have been efforts to develop therapeutic drugs targeting Aβ, culminating in the approval of a humanized anti-soluble aggregated Aβ monoclonal antibody in Japan. The effectiveness of such medications hinges on the early detection of Aβ accumulation in individuals with mild cognitive impairment, ideally prior to the manifestation of symptoms. The identification of brain Aβ accumulation relies on expensive and invasive methods such as positron emission tomography (amyloid PET) and cerebrospinal fluid testing (CSF testing). These tests are limited to select medical institutions and come with considerable financial and procedural burdens. Consequently, there has been a persistent quest for an affordable and user-friendly screening method to identify candidates necessitating amyloid PET or CSF testing. While previous studies have attempted to predict brain Aβ accumulation using cognitive function tests, blood tests, and brain imaging, this machine learning study represents a pioneering approach centering on “biological data” and “lifestyle data.” The machine learning breakthrough The collaboration between Oita University and Eisai Co. has given rise to a pioneering machine learning model capable of predicting brain Aβ accumulation. What sets this model apart is its utilization of data from wristband sensors, a departure from conventional cognitive and…
Filed under: News - @ January 2, 2024 5:12 pm