Artificial Intelligence Model Can Detect Parkinson's From Breathing Patterns - Neuroscience News

Summary: A newly developed artificial intelligence model can detect Parkinson's disease by reading a person's breathing patterns. The algorithm can also discern the severity of Parkinson's disease and track progression over time. Parkinson's disease is notoriously difficult to diagnose as it relies primarily on the appearance of motor symptoms such as tremors, stiffness, and slowness, but these symptoms often appear several years after the disease onset. Now, Dina Katabi, the Thuan (1990) and Nicole Pham Professor in the Department of Electrical Engineering and Computer Science (EECS) at MIT and principal investigator at MIT Jameel Clinic, and her team have developed an artificial intelligence model that can detect Parkinson's just from reading a person's breathing patterns. The tool in question is a neural network, a series of connected algorithms that mimic the way a human brain works, capable of assessing whether someone has Parkinson's from their nocturnal breathing--i.e., breathing patterns that occur while sleeping.