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


The new technology can be used at home while the patient sleeps.

MIT researchers have developed a technology that will allow patients to diagnose Alzheimer’s disease early and at home. Bloomberg

A new MIT-developed artificial intelligence model can detect early Parkinson’s disease — which is notoriously difficult to diagnose — from a person’s breathing patterns, the university announced Monday.

A news release about the technology states that Parkinson’s disease is difficult to diagnose because it depends primarily on the presence of motor symptoms such as tremors, stiffness and slowness, which often appear many years after the onset of the disease.

But MIT electrical engineering and computer science professor Dina Katabi 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 technology is a neural network – a series of connected algorithms that mimic the way the human brain works – able to assess whether someone has Parkinson’s by how they breathe while they sleep.

The neural network, trained by MIT PhD student Yuzhe Yang and postdoc Yuan Yuan, is also able to detect the severity of someone’s Parkinson’s and track the progression of their disease over time, the release said.

“The connection between Parkinson’s and respiration was noted as early as 1817 in the work of Dr. James Parkinson. This prompted us to consider the possibility of detecting the disease from respiration without looking at movement,” Katabi said in the statement.

“Some medical studies have shown that respiratory symptoms manifest years before motor symptoms, meaning that respiratory characteristics before Parkinson’s diagnosis may be promising for risk assessment.”

Over the years, researchers have tried to diagnose Parkinson’s 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 repeated testing, which may allow for early diagnosis and continuous tracking of disease progression.

But the researchers knew that with the new AI model, the detection of Parkinson’s disease could be done every night at home while the patient is sleeping 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 the reflection of the surrounding environment, and monitors a person’s breathing pattern without any physical contact, the release said. The respiratory signal is then fed to a neural network to assess Parkinson’s.

The research team’s algorithm was then tested on 7,687 people, including 757 Parkinson’s patients.

The fastest growing neurological disease in the world, Parkinson’s is the second most common neurological disorder after Alzheimer’s disease, the release said. More than 1 million people in the US alone are living with this disease.

“In the context of clinical care, the approach may aid in the assessment of Parkinson’s patients in traditionally underserved communities, including those who live in rural areas and have difficulty leaving home due to limited mobility or cognitive impairment,” Katabi said in the statement.

Yang is the first author and Katabi is the senior author of a new paper describing the technique, published Monday. Nature medicine. Other authors include researchers from Rutgers University, University of Rochester Medical Center, Mayo Clinic, Massachusetts General Hospital, and Boston University.

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