Parkinson's disease is notoriously difficult to diagnose because it relies primarily on the onset of motor symptoms such as tremors, stiffness and dullness, but these tend to appear years after the onset of symptoms. Now, Professor Dina Katabi and her team from MIT's Department of Electrical Engineering and Computer Science have developed an artificial intelligence model that can detect Parkinson's disease simply by reading a person's breathing patterns. The tool is a neural network, a series of associative algorithms that mimic the way the human brain works, and is able to assess whether a person has Parkinson's disease from their nighttime breathing -- the breathing pattern during sleep.

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