Software uses selfies to detect early symptoms of Parkinson's disease

Machine learning lets Rochester researchers accurately identify signs of the neurological disease by analyzing facial muscles. Ehsan Hoque, Ph.D., associate professor of Computer Science at the University of Rochester, and collaborators developed algorithms to analyze brief videos, including short clips while taking selfies, to detect subtle movements in facial muscles that are not visible to the naked eye. The study, published in Nature Digital Medicine, found this software can then predict with remarkable accuracy whether a person who takes a selfie is likely to develop Parkinson’s disease; it is as reliable as current technology used to make a similar prediction such as expensive, wearable digital biomarkers that monitor motor symptoms.