Researchers propose a data-driven strategy to stratify risk of progression from mild cognitive impairment to Alzheimer's disease

The projected cost of caring for millions of individuals who have Alzheimer's disease (AD) worldwide will exceed a $1 trillion in a few years. In addition to the enormous health burden, patients and their caregivers experience financial, physical and psychological strain. A theory regarding repeated drug failure in AD is that patients undergoing experimental therapies are selected too late in the disease process. Therefore, it is important to identify patients at a high risk of progression to AD in early stages of the disease. To help identify persons who could benefit from early interventions, researchers from Boston University have developed a deep learning framework that can stratify individuals with mild cognitive impairment (MCI) based on their risk of advancing to AD.

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