Parkinson’s disease is notoriously difficult to diagnose as it is based primarily on the appearance of motor symptoms such as tremors, stiffness and slowness, but these symptoms often appear several years after the onset of the disease. Now, Dina Katabi, the Thuan (1990) and Nicole Pham Professor in MIT’s Department of Electrical Engineering and Computer Science (EECS) and a principal investigator at the MIT Jameel Clinic, and her team have developed an artificial intelligence model that can detect Parkinson’s just by reading a person’s breathing patterns.
The tool in question is a neural network, a series of connected algorithms that mimic the functioning of the human brain, capable of assessing whether someone has Parkinson’s from their nocturnal breathing, that is, the breathing patterns that occur while sleeping. The neural network, which was trained by MIT doctoral student Yuzhe Yang and postdoc Yuan Yuan, is also capable of discerning the severity of someone’s Parkinson’s disease and tracking the progression of their disease over time.
Yang and Yuan are co-authors of a new paper describing the work, published today in Natural medicine. Katabi, who is also an affiliate of the MIT Computer Science and Artificial Intelligence Laboratory and director of the Center for Wireless Networks and Mobile Computing, is the lead author. They are joined by 12 colleagues from Rutgers University, the University of Rochester Medical Center, the Mayo Clinic, Massachusetts General Hospital and the Boston University School of Health and Rehabilitation.
Over the years, researchers have investigated the potential to detect Parkinson’s using cerebrospinal fluid and neuroimaging, but these methods are invasive, expensive, and require access to specialized medical centers, making them unsuitable for frequent tests that would otherwise Thus, they could provide an early diagnosis or continuous monitoring of the disease. progressive disease.
The MIT researchers showed that the artificial intelligence assessment of Parkinson’s can be done every night at home while the person sleeps and without touching their body. To do so, the team developed a device that looks like a home Wi-Fi router, but instead of providing Internet access, the device emits radio signals, analyzes their reflections in the surrounding environment, and extracts the breathing patterns of the subject without any bodily intervention. Contact. The breathing signal is then sent to the neural network to assess Parkinson’s passively, and no effort is needed from the patient and caregiver.
“As early as 1817, in the work of Dr. James Parkinson, a relationship between Parkinson’s and breathing was noted. This motivated us to consider the potential of detecting disease from breathing without looking at movements,” says Katabi. “Some medical studies have shown that respiratory symptoms manifest years before motor symptoms, which means that respiratory attributes could hold promise for risk assessment prior to Parkinson’s diagnosis.”
The fastest growing neurological disease in the world, Parkinson’s is the second most common neurological disorder, after Alzheimer’s disease. In the United States alone, it affects more than 1 million people and has an annual economic burden of $51.9 billion. The research team’s device was tested on 7,687 people, including 757 Parkinson’s patients.
Katabi notes that the study has important implications for drug development and clinical care for Parkinson’s. “In terms of drug development, the results may enable clinical trials with significantly shorter duration and fewer participants, ultimately speeding up the development of new therapies. In terms of clinical care, the approach can help in the evaluation 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 decline,” she says.
“We haven’t had therapeutic breakthroughs this century, which suggests that our current approaches to evaluating new treatments are suboptimal,” says Ray Dorsey, a professor of neurology at the University of Rochester and a Parkinson’s specialist who co-authored the paper. Dorsey adds that the study is probably one of the largest sleep studies ever conducted on Parkinson’s. “We have very limited information about the manifestations of the disease in its natural environment and [Katabi’s] The device allows you to get objective, real-world assessments of how people are doing at home. The analogy I like to draw [of current Parkinson’s assessments] it’s a lamppost at night, and what we see from the lamppost is a very small segment… [Katabi’s] The completely contactless sensor helps us illuminate the dark.”
This research was conducted in collaboration with the University of Rochester, Mayo Clinic, and Massachusetts General Hospital, and is sponsored by the National Institutes of Health, with partial support from the National Science Foundation and the Michael J. Fox Foundation.