We aimed to develop a Deep Learning algorithm to extract the most important brain image features that are invariant across health-centers for classifying between patients with Alzheimer’s Disease and patients with behavioral variant Frontotemporal Dementia. Then we will test the algorithm in research and clinical datasets obtained in Latin America to assess the generalizability of its predictions. Unlike other traditional machine learning approaches that are out of reach of LAC because of the cost and unavailability of specialized personnel, the specific goal of this pilot project is to create and optimize a more advanced deep learning algorithm based on magnetic resonance imaging automatic feature selection, while validating its diagnostic accuracy on heterogeneous data obtained in different health centers from LAC.
PI: Sebastian Moguilner
Support: Alzheimer’s Association and GBHI
