What the science says about milk, sleep, and if your cat really loves you. Breakthroughs, discoveries, and DIY tips sent every weekday. Cats are man's best friend--never mind that other animal species. Jokes aside, humans and cats have lived together for thousands of years but not nearly as long as humans and dogs . It makes sense, then, that we don't always understand cats very well.

The ability to assess sleep at home, capture sleep stages, and detect the occurrence of apnea (without on-body sensors) simply by analyzing the radio waves bouncing off people's bodies while they sleep is quite powerful. Such a capability would allow for longitudinal data collection in patients' homes, informing our understanding of sleep and its interaction with various diseases and their therapeutic responses, both in clinical trials and routine care. In this article, we develop an advanced machine learning algorithm for passively monitoring sleep and nocturnal breathing from radio waves reflected off people while asleep. Validation results in comparison with the gold standard (i.e., polysomnography) (n=849) demonstrate that the model captures the sleep hypnogram (with an accuracy of 81% for 30-second epochs categorized into Wake, Light Sleep, Deep Sleep, or REM), detects sleep apnea (AUROC = 0.88), and measures the patient's Apnea-Hypopnea Index (ICC=0.95; 95% CI = [0.93, 0.97]). Notably, the model exhibits equitable performance across race, sex, and age. Moreover, the model uncovers informative interactions between sleep stages and a range of diseases including neurological, psychiatric, cardiovascular, and immunological disorders. These findings not only hold promise for clinical practice and interventional trials but also underscore the significance of sleep as a fundamental component in understanding and managing various diseases.

Doctors could soon evaluate Parkinson's disease by having patients do one simple thing--sleep. A new study led by MIT researchers trains a neural network to analyze a person's breathing patterns while sleeping and determine whether the subject has Parkinson's. Recently published in Nature Medicine, the work could lead to earlier detection and treatment. "Our goal was to create a method for detecting and assessing Parkinson's disease in a reliable and convenient way. Inspired by the connections between Parkinson's and breathing signals, which are high-dimensional and complex, a natural choice was to use the power of machine learning to diagnose and track the progression," said lead author Yuzhe Yang, a PhD student at MIT's Computer Science & Artificial Intelligence Laboratory. While notoriously difficult to pinpoint, Parkinson's has become the fastest-growing neurological disease globally.

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.

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.