Parkinson’s disease is notoriously challenging to diagnose since it mostly depends on the emergence of motor symptoms like tremors, stiffness, and slowness—symptoms that frequently develop years after the disease first manifests itself. Dina Katabi, the principal investigator at the MIT Jameel Clinic and the Thuan (1990) and Nicole Pham Professor in the Department of Electrical Engineering and Computer Science (EECS) at MIT, has made an artificial intelligence model that can tell if someone has Parkinson’s disease just by watching how they breathe.
The gadget in question is a neural network, which is made up of interconnected algorithms that simulate how the human brain functions. It can determine if a person has Parkinson’s disease based on their nocturnal breathing or breathing patterns that happen when they are asleep. The neural network, which Yuzhe Yang, an MIT PhD student, and Yuan Yuan, a postdoc, trained, can also determine a person’s Parkinson’s disease severity and monitor the disease’s development over time.
Yang is the first author of a new publication outlining the research that was just published in Nature Medicine. The senior author is Katabi, head of the Center for Wireless Networks and Mobile Computing and a member of the MIT Computer Science and Artificial Intelligence Laboratory. Yuan and 12 other coworkers from the Mayo Clinic, the University of Rochester Medical Center, the Massachusetts General Hospital, and the Boston University College of Health and Rehabilitation are also a part of the group.
Cerebrospinal fluid and neuroimaging have been looked at as possible screening tools for Parkinson’s disease over the years, but they are invasive, expensive, and require access to specialized medical facilities, which makes them unsuitable for routine testing that would otherwise allow for early diagnosis or monitoring of the disease’s progression.
The MIT researchers showed that a Parkinson’s assessment using artificial intelligence may be carried out each night at home while the patient is sleeping and without having to touch them. To achieve this, the researchers created a device that resembles a Wi-Fi router for a home, but instead of giving internet access, it emits radio signals, analyzes how they are reflected off the environment, and then, without any physical touch, extracts the subject’s breathing patterns. Since the breathing signal is then sent to the neural network, neither the patient nor the caregiver has to do anything to check for Parkinson’s.
“Parkinson’s disease and breathing have been linked since Dr. James Parkinson’s research in 1817. This made us think about whether the condition could be diagnosed from a person’s breathing without watching them move, Katabi says.Some medical research shows that respiratory symptoms show up years before motor symptoms. This suggests that the way a person breathes could be used to predict risk before Parkinson’s disease is found.
Parkinson’s disease, the second most prevalent neurological disorder after Alzheimer’s disease, is the neurological condition with the fastest global growth rate. It affects more than 1 million people in the US alone, putting a $51.9 billion annual strain on the economy. The algorithm developed by the study team was tested on 7,687 people, including 757 Parkinson’s patients.
According to Katabi, the study has significant ramifications for clinical treatment and drug development for Parkinson’s disease. The findings could speed up the creation of novel treatments by enabling clinical trials with a lot fewer participants and much shorter durations. Regarding clinical care, the method can assist with the evaluation of Parkinson’s patients in traditionally underserved regions, such as those who reside in rural areas or have trouble leaving their homes due to poor mobility or cognitive impairment, “she claims.
According to co-author and University of Rochester professor of neurology Ray Dorsey, “We haven’t had any therapeutic breakthroughs this century, suggesting that our existing procedures to evaluate new medicines are unsatisfactory.” Dorsey also specializes in Parkinson’s disease. The study, according to Dorsey, is probably one of the biggest sleep studies on Parkinson’s ever carried out. “We know very little about how the disease manifests in the real world, and [Katabi’s] technology lets you get unbiased, practical evaluations of how people are doing at home. I like to compare current Parkinson’s assessments to a streetlamp that is on at night. What we can see from the streetlamp is only a very small portion of the scene. We are able to see better in the dark because of [Katabi’s] fully contactless sensor. ”
The National Institutes of Health financed this study, which was carried out in association with the University of Rochester, the Mayo Clinic, and Massachusetts General Hospital. The National Science Foundation and the Michael J. Fox Foundation also provided some funding.
