A device developed by the Massachusetts Institute of Technology (MIT), in the United States, with the appearance of a Wi-Fi router uses a neural network to discern the presence and severity of Parkinson’s, one of the fastest growing neurological diseases in the world. Parkinson’s disease is notoriously difficult to diagnose as it relies primarily on appearance of motor symptoms such as tremors, rigidity and slownessbut these symptoms usually appear several years after the onset of the disease.
Artificial intelligence assessment of Parkinson’s can be done every night at home while the person is asleep and without touching their body
Now, this team of researchers has 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 a human braincapable of assessing whether someone has Parkinson’s based on their nocturnal breathing, that is, the breathing patterns that occur while sleeping.
The neural network is also capable of discern the severity of someone’s Parkinson’s disease and follow the progression of your disease over time. Over the years, researchers have investigated the potential to detect Parkinson’s using cerebrospinal fluid and neuroimaging, but such methods are invasive, expensive and require access to specialized medical centers, making them unsuitable for routine testing that might otherwise provide early diagnosis or ongoing monitoring of disease progression.
The MIT researchers showed that the artificial intelligence assessment of Parkinson’s can be done every night at home while the person is asleep and without touching their body. To do this, 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 subject’s breathing patterns without any bodily contact. The breathing signal is then fed into the neural network to assess Parkinson’s passively, and no effort is required on the part of the patient and caregiver.
Parkinson’s is the second most common neurological disorder, after Alzheimer’s disease.
“A link between Parkinson’s and breathing was noted as early as 1817 in the work of Dr. James Parkinson. This prompted us to consider the potential of detecting the disease from breathing without looking at movement. Some medical studies have shown that respiratory symptoms manifest years before motor symptoms, which means that respiration attributes could hold promise for risk assessment before Parkinson’s diagnosis,” says Dina Katabi, one of the leaders of the research, which has been published in the scientific journal Nature Medicine.
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 a million people and has an annual economic burden of $51.9 billion. The research team’s algorithm was tested in 7,687 individuals, including 757 Parkinson’s patients.
Katabi notes that the study has Important implications for Parkinson’s drug development and clinical care. “In terms of drug development, the results may enable clinical trials with significantly shorter durations and fewer participants, ultimately accelerating the development of new therapies. In terms of clinical care, the approach may aid in the assessment of patients.” with Parkinson’s in traditionally underserved communities, including those living in rural areas and those with difficulty leaving home due to limited mobility or cognitive decline,” he says.
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