Fox News' Dr. Marc Siegel has the latest on the treatment of the brain disease on'America Reports.' A new blood test could reveal Parkinson's diagnoses up to seven years before symptoms emerge. Researchers from University College London and University Medical Center Goettingen in Germany used artificial intelligence to develop the test. The study, which was published in the journal Nature Communications, included 72 patients with rapid eye movement behavior disorder (iRBD), a condition that has been linked to a higher risk of Alzheimer's. When the researchers used machine learning to analyze blood samples from the patients, they discovered that 79% of them had the same biomarkers as people with Parkinson's.

With Artificial Intelligence (AI) and Machine Learning (ML), the healthcare industry is continuing to undergo a transformation. Valued at US$10.4bn last year, the global artificial intelligence (AI) in healthcare market is expected to continue to grow at a compound annual growth rate (CAGR) of 38.4% from 2022 to 2030. And with breakthroughs such as a report that AI could be used to identify conditions such as Parkinson's disease years before the appearance of physical symptoms, there appears to be a healthy future for the relationship between technology and medicine. Researchers at MIT have developed an artificial intelligence model that can detect Parkinson's just from reading a person's breathing patterns while they are sleeping. Parkinson's disease is hard to diagnose, researchers say, because it relies primarily on the appearance of motor symptoms, such as tremors, stiffness, and slowness, which can often appear several years after the disease onset.

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.

In a recent Nature Medicine journal study, researchers develop an artificial intelligence (AI)-based model to detect Parkinson's disease (PD) and track its progression from nocturnal breathing signals. Since PD is the fastest-growing neurological disease worldwide, there is an urgent need for novel diagnostic biomarkers that can detect the disease at an early stage. Currently, there are no drugs capable of reversing or ceasing PD progression. Furthermore, PD is typically diagnosed based on changes in motor functions, such as tremors and rigidity. The assessment of PD progression primarily relies on patient self-reporting; however, clinicians also use the Movement Disorder Society Unified Parkinson's Disease Rating Scale (MDS-UPDRS) for qualitative PD assessment. Some existing PD biomarkers, including cerebrospinal fluid, blood biochemical, and neuroimaging, have shown promising results for their potential utility in the early diagnosis of this disease.

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 and Yuan are co-first authors on a new paper describing the work, published today in Nature Medicine.