People with paralysis can control robotic devices through thought alone. Researchers at UC San Francisco have achieved a remarkable breakthrough in brain-computer interface (BCI) technology, enabling individuals with paralysis to control robotic devices through thought alone. This innovation combines artificial intelligence (AI) with neuroscience, allowing a paralyzed man to manipulate a robotic arm by imagining movements, a feat that marks a significant milestone in restoring autonomy to people with severe motor impairments. The device, known as a brain-computer interface (BCI), represents a fusion of advanced AI and neural engineering. BCIs have previously struggled to maintain functionality over extended periods, often losing effectiveness after just one or two days.

You are free to share this article under the Attribution 4.0 International license. Using machine learning to sift through the electronic health records of both mothers and newborns can predict how premature babies will fare in their first two months of life, researchers report. The new method, reported in the journal Science Translational Medicine, allows physicians to classify, at or before birth, which infants are likely to develop complications of prematurity. "Preterm birth is the single largest cause of death in children under age 5 worldwide." "This is a new way of thinking about preterm birth, placing the focus on individual health factors of the newborns rather than looking only at how early they are born," says senior author Nima Aghaeepour, an associate professor of anesthesiology, perioperative and pain medicine and of pediatrics Stanford University School of Medicine.

By sifting through electronic health records of moms and babies using a machine-learning algorithm, scientists can predict how at-risk newborns will fare in their first two months of life. The new method allows physicians to classify, at or before birth, which infants are likely to develop complications of prematurity. A study describing the method, developed at the Stanford School of Medicine, was published online Feb. 15 in Science Translational Medicine. "This is a new way of thinking about preterm birth, placing the focus on individual health factors of the newborns rather than looking only at how early they are born," said senior study author Nima Aghaeepour, PhD, an associate professor of anesthesiology, perioperative and pain medicine and of pediatrics. The study's lead authors are postdoctoral scholar Davide De Francesco, PhD, and Jonathan Reiss, MD, an instructor in pediatrics.

A team of researchers lead by Brittany Dugger of UC Davis Health has been awarded a $3.8 million grant from the National Institute for Aging (NIA) to help define the neuropathology of Alzheimer's disease in Hispanic cohorts. The five-year grant will fund the first large-scale initiative to present a detailed description of brain manifestations of the Alzheimer's disease in individuals of Mexican, Cuban, Puerto Rican, and Dominican descent. Hispanics, one of the fastest growing demographic groups in the U.S., have a higher risk of dementia than Non-Hispanic Whites (NHWs). The demographic, genetic and environmental differences between individuals of Hispanic descent and NHWs can lead to different levels of disease risk and presentation. "There is little information on the pathology of dementia affecting people from minority groups, especially for individuals of Mexican, Cuban, Puerto Rican, and Dominican descent," said Brittany Dugger, assistant professor at the Department of Pathology and Laboratory Medicine at UC Davis School of Medicine in Sacramento.

A team of researchers lead by Brittany Dugger of UC Davis Health has been awarded a $3.8 million grant from the National Institute for Aging (NIA) to help define the neuropathology of Alzheimer's disease in Hispanic cohorts. The five-year grant will fund the first large-scale initiative to present a detailed description of brain manifestations of the Alzheimer's disease in individuals of Mexican, Cuban, Puerto Rican, and Dominican descent. Hispanics, one of the fastest growing demographic groups in the U.S., have a higher risk of dementia than Non-Hispanic Whites (NHWs). The demographic, genetic and environmental differences between individuals of Hispanic descent and NHWs can lead to different levels of disease risk and presentation. "There is little information on the pathology of dementia affecting people from minority groups, especially for individuals of Mexican, Cuban, Puerto Rican, and Dominican descent," said Brittany Dugger, assistant professor at the Department of Pathology and Laboratory Medicine at UC Davis School of Medicine in Sacramento.

Researchers at UC Davis and UC San Francisco have found a way to teach a computer to precisely detect one of the hallmarks of Alzheimer's disease in human brain tissue, delivering a proof of concept for a machine-learning approach capable of automating a key component of Alzheimer's research. Amyloid plaques are clumps of protein fragments in the brains of people with Alzheimer's disease that destroy nerve cell connections. Much like the way Facebook recognizes faces based on captured images, the machine learning tool developed by a team of University of California scientists can "see" if a sample of brain tissue has one type of amyloid plaque or another -- and do it very quickly. The findings, published May 15, 2019 in Nature Communications, suggest that machine learning can augment the expertise and analysis of an expert neuropathologist. The tool allows them to analyze thousands of times more data and ask new questions that would not be possible with the limited data processing capabilities of even the most highly trained human experts. "We still need the pathologist," said Brittany N. Dugger, PhD, an assistant professor in the UC Davis Department of Pathology and Laboratory Medicine at UC Davis and lead author of the study.

Google may one day be able to predict when you'll die years in advance. The firm has created an AI that it claims is 95 per cent accurate in predicting whether hospital patients will pass away 24 hours after admission. This is around 10 per cent better than traditional models. To make its predictions, the software uses data such as patient's ethnicity, age, gender, previous diagnoses, lab results and vital signs. But what makes it so powerful is that it includes data previously thought out of reach of machines, such as doctor notes buried in PDFs or scribbled on old charts.

Most of us will try to diagnose an illness by doing a quick Google search. But now, it seems, Dr Google is set to get an upgrade. The firm is working on technology that can predict when you are about to get sick. Google Brain, the company's artificial intelligence research project, is analysing medical data to better understand people's health and what provisions they may need in the future. Hospitals produce a huge amount of patient data.

Would you trust an algorithm to help you with a medical diagnosis? As hospitals seek out new tools to assist them in triaging the patients most in need, technologies driven by machine learning are expected to make a big impact in the medical sector. The latest company to throw its hat into the ring is General Electric, which is investing big in software and is already known for its medical imaging equipment. The manufacturing giant exclusively shared with Fast Company that it is partnering with UC San Francisco for the next three years to develop a set of algorithms to help its radiologists distinguish between a normal result and one that requires further attention. "There's tremendous opportunity to look at large datasets, like medical images, to predict how patients will do," says UC San Francisco's director of UCSF's Center for Digital Health Innovation, Michael Blum. It's early days, but machine learning and deep learning technologies are already making their way into a small number of medical specialties, including primary care, pathology, and radiology.