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Artificial intelligence model can detect Parkinson’s from breathing patterns

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.
MIT News
A man lying down with an android standing next to him in a thinking pose with various anatomical diagrams in the background.
A new neural network trained by MIT PhD student Yuzhe Yang and postdoc Yuan Yuan assesses whether or not someone has Parkinson’s from their nocturnal breathing.
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