Researchers at MIT have developed an AI system that can diagnose Parkinson's disease and track its progression, simply by monitoring someone's breathing patterns as they sleep. The device looks like an internet router and can be mounted on the wall in a bedroom. It emits radio waves and then a neural network analyzes the reflected waves to assess breathing patterns. Crucially, the technology may be able to assist in diagnosing Parkinson's disease much earlier than many conventional techniques and it is highly convenient and non-invasive compared with traditional diagnostics. It may also be particularly beneficial in testing new treatments for Parkinson's as a non-invasive method to monitor disease progression.

Neurological disorders are some of the leading sources of disability globally and Parkinson's disease is the fastest-growing neurological disease in the world. Parkinson's is difficult to diagnose as diagnosis primarily relies on the appearance of symptoms like tremors and slowness but these symptoms usually appear several years after the onset of the disease. The model also estimated the severity and progression of Parkinson's, in accordance with the Movement Disorder Society Unified Parkinson's Disease Rating Scale (MDS-UPDRS), which is the standard rating scale used clinically. The research findings have been published in the journal Nature Medicine. The researchers trained the model by using nocturnal breathing data (data collected while subjects were asleep) from various hospitals in the US and some public datasets.

Summary: A newly developed artificial intelligence model can detect Parkinson's disease by reading a person's breathing patterns. The algorithm can also discern the severity of Parkinson's disease and track progression over time. 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. The tool in question is a neural network, a series of connected algorithms that mimic the way a human brain works, capable of assessing whether someone has Parkinson's from their nocturnal breathing--i.e., breathing patterns that occur while sleeping.

An at-home, artificial intelligence-based system identified individuals with Parkinson's disease and predicted disease severity and progression using nocturnal breathing signals, according to a study in Nature Medicine. "A relationship between Parkinson's and breathing was noted as early as 1817, in the work of Dr. James Parkinson," Dina Katabi, PhD, principal investigator at the MIT Jameel Clinic, said in a related MIT press release. "This motivated us to consider the potential of detecting the disease from one's breathing without looking at movements. "Some medical studies have shown that respiratory symptoms manifest years before motor symptoms, meaning that breathing attributes could be promising for risk assessment prior to Parkinson's diagnosis." Katabi and colleagues evaluated the AI model using a dataset of 7,671 individuals from several sources, including the Mayo Clinic, Massachusetts General Hospital sleep lab and observational clinical trials.

A new MIT-developed artificial intelligence model can make an early detection of Parkinson's Disease -- which is notoriously hard to diagnose -- from a person's breathing patterns, the university announced Monday. A news release about the technology said that Parkinson's disease is hard to diagnose because it relies primarily on the appearance of motor symptoms, such as tremors, stiffness, and slowness, which often appear several years after the disease onset. But Dina Katabi, an MIT electrical engineering and computer science professor, 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 tech is a neural network -- a series of connected algorithms that mimic the way a human brain works -- capable of assessing whether someone has Parkinson's from 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 discern the severity of someone's Parkinson's and track the progression of their disease over time, the release said.

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. The tool in question is a neural network, a series of connected algorithms that mimic the way a human brain works, capable of assessing whether someone has Parkinson's from their nocturnal breathing -- i.e., breathing patterns that occur while sleeping. The neural network, which was trained by MIT PhD student Yuzhe Yang and postdoc Yuan Yuan, is also able to discern the severity of someone's Parkinson's disease and track the progression of their disease over time. Yang is first author on a new paper describing the work, published today in Nature Medicine.

James Parkinson first flagged a link between changes in breathing patterns and the debilitating disease that now bears his name. But since his work in the early 19th century, only minimal progress has been made in treating a condition that has become alarmingly prevalent. A study published Monday offers a glimmer of new hope. The paper by researchers at the Massachusetts Institute of Technology and several other institutions describes an artificial intelligence tool that can analyze changes in nighttime breathing to detect and track the progression of disease, which causes tremors and other serious issues with movement. The AI was able to accurately flag Parkinson's using one night of breathing data collected from a belt worn around the abdomen or from a passive monitoring system that tracks breathing using a low-power radio signal.

A compelling new study indicates Parkinson's disease (PD) could be diagnosed by remotely tracking a person's breathing patterns. Led by researchers from MIT, the study presents an AI system that uses radio waves to monitor breathing while a person sleeps. Dina Katabi, principal investigator on the new research, said the study was inspired by 200-year-old observations from James Parkinson, the first doctor to clinically catalog signs of the degenerative neurological disease. "A relationship between Parkinson's and breathing was noted as early as 1817, in the work of Dr. James Parkinson," explained Katabi. "This motivated us to consider the potential of detecting the disease from one's breathing without looking at movements. Some medical studies have shown that respiratory symptoms manifest years before motor symptoms, meaning that breathing attributes could be promising for risk assessment prior to Parkinson's diagnosis."

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. The tool in question is a neural network, a series of connected algorithms that mimic the way a human brain works, capable of assessing whether someone has Parkinson's from their nocturnal breathing -- i.e., breathing patterns that occur while sleeping. The neural network, which was trained by MIT PhD student Yuzhe Yang and postdoc Yuan Yuan, is also able to discern the severity of someone's Parkinson's disease and track the progression of their disease over time. Yang and Yuan are co-first authors on a new paper describing the work, published today in Nature Medicine.

The MIT Stephen A. Schwarzman College of Computing has awarded two inaugural chaired appointments to Dina Katabi and Aleksander Madry in the Department of Electrical Engineering and Computer Science (EECS). "These distinguished endowed professorships recognize the extraordinary achievements of our faculty and future potential of their academic careers," says Daniel Huttenlocher, dean of the MIT Schwarzman College of Computing and the Henry Ellis Warren Professor of Electrical Engineering and Computer Science. "I'm delighted to make these appointments and acknowledge Dina and Aleksander for their contributions to MIT, the college, and EECS, and their efforts to advance research and teaching in computer science, electrical engineering, artificial intelligence, and machine learning." Dina Katabi is the inaugural Thuan (1990) and Nicole Pham Professor. Katabi is being honored as an exceptional faculty member and for her commitment to mentoring students.