Artificial Intelligence May Find Signs Of Alzheimer's In Neuroimaging Data

Shuiwang Ji, associate professor in the Department of Computer Science and Engineering at Texas A&M University, is one of the principal investigators on a $6 million grant from the National Institutes of Health to develop artificial intelligence-driven methods to automate the process of finding subtle telltale signs of Alzheimer's disease in neuroimaging data. Ji will lead the research team tasked with developing advanced deep-learning methods for finding relevant neural signatures lurking within neuroimages taken using different techniques, such as PET scans and MRIs. "I feel very excited with this collaborative opportunity to make scientific discoveries in medical domains using deep learning and artificial intelligence," said Ji, who has extensive expertise in machine learning, deep learning and medical image analysis. Alzheimer's disease affects 5.6 million Americans over the age of 65, and its symptoms are most noticeably the progressive impairment of cognitive and memory functions. It is also currently the most common form of dementia in the elderly.