A new artificial intelligence model developed by the Massachusetts Institute of Technology can make early detection of Parkinson’s disease — which is extremely difficult to diagnose — from a person’s breathing patterns, the university said Monday.
A news release about the technology said Parkinson’s disease is difficult to diagnose because it relies heavily on the onset of motor symptoms, such as tremors, stiffness and slowness, which often appear several years after the onset of the disease.
But Dina Katabi, a professor of electrical engineering and computer science at MIT, and her team have already developed an artificial intelligence model that can detect Parkinson’s disease from a person’s breathing patterns, the release said.
The technology is a neural network – a series of linked algorithms that mimic the way the human brain works – capable of judging whether someone has Parkinson’s by 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 recognize the severity of someone’s Parkinson’s disease and track the progression of their disease over time, the release said.
“The link between Parkinson’s disease and breathing was noted as early as 1817 in the work of Dr. James Parkinson. This motivated us to consider the potential to detect the disease from breathing without looking at movements,” Katabi said in the release.
“Some medical research shows that respiratory symptoms appear years before motor symptoms, which means that breathing characteristics may hold promise for risk assessment before Parkinson’s diagnosis.”
Over the years, researchers have tried to detect Parkinson’s disease using cerebrospinal fluid and neuroimaging, but such methods are invasive, expensive and require access to specialized medical centers, the release said. This makes these methods unsuitable for frequent testing that could allow early diagnosis and continuous monitoring of disease progression.
But the researchers knew that with the new AI model, Parkinson’s detection could be done every night at home while the patient sleeps and without touching their body.
So they developed a device that looks like a Wi-Fi router, but instead of providing Internet access, the device emits radio signals, analyzes reflections from the surrounding environment and monitors a person’s breathing patterns without any physical contact, the release said. The respiratory signal is then fed to the neural network to be evaluated for Parkinson’s disease.
The research team’s algorithm was then tested on 7,687 individuals, including 757 Parkinson’s patients.
The world’s fastest growing neurological disease, Parkinson’s disease is the second most common neurological disease after Alzheimer’s disease, the release said. In the US alone, over a million people live with the disease.
“In terms of clinical care, the approach may help in the assessment of Parkinson’s patients in traditionally underserved communities, including those living in rural areas and those with difficulty leaving home due to limited mobility or cognitive impairment,” Katabi said in the message.
Yang is the first author and Kataby is the senior author of a new paper describing the technology, published Monday in the Natural medicine. Other authors include researchers from Rutgers University, the University of Rochester Medical Center, the Mayo Clinic, Massachusetts General Hospital and Boston University.
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