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Predicting Amyloid: AI for Early Detection
10 février 2025
#Alzheimer’s #MachineLearning #Neurology #Screening #AmyloidPlaque
The accumulation of amyloid plaques in the brain is a key pathological feature of Alzheimer’s disease. These plaques often appear years, even decades, before cognitive symptoms emerge. Their accumulation is associated with an increased risk of cognitive decline, making them a priority target for screening and early intervention. However, current detection tools, such as lumbar puncture for biomarker measurement in cerebrospinal fluid (CSF) or positron emission tomography (PET) imaging, are invasive, expensive, and not widely accessible, limiting their use in asymptomatic populations.
The accumulation of amyloid plaques in the brain is a key pathological feature of Alzheimer’s disease. These plaques often appear years, even decades, before cognitive symptoms emerge. Their accumulation is associated with an increased risk of cognitive decline, making them a priority target for screening and early intervention. However, current detection tools, such as lumbar puncture for biomarker measurement in cerebrospinal fluid (CSF) or positron emission tomography (PET) imaging, are invasive, expensive, and not widely accessible, limiting their use in asymptomatic populations.
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Scientific reference
Le Scouarnec, L., et al. (2024). Development and assessment of algorithms for predicting brain amyloid positivity in a population without dementia. Alzheimer's Research & Therapy, 16(1), 219
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