MIT's new artificial intelligence technology can detect Parkinson's early using breathing patterns

A new MIT-developed artificial intelligence model can make an early detection of Parkinson's Disease -- which is notoriously hard to diagnose -- from a person's breathing patterns, the university announced Monday. A news release about the technology said that Parkinson's disease is hard to diagnose because it relies primarily on the appearance of motor symptoms, such as tremors, stiffness, and slowness, which often appear several years after the disease onset. But Dina Katabi, an MIT electrical engineering and computer science professor, and her team have now developed an artificial intelligence model that can detect Parkinson's from a person's breathing patterns, the release said. The tech 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 how they breathe while they sleep. The neural network, which was trained by MIT PhD student Yuzhe Yang and postdoc Yuan Yuan, is also able to discern the severity of someone's Parkinson's and track the progression of their disease over time, the release said.