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.
