This soft, robotic pump could revive a failing heart by squeezing half of it gently - saving patients from a life-time on anticoagulant medication. Currently, the only pump machines available are made of tough metal, and blood has to run through the pump, exposing it to the unnatural material. This new device comprises three parts: a c-shaped'frame' that goes around the heart, an'anchor' that sticks into the heart to hold the frame in place, and a soft rubber band that replicates a muscle. It has proved a success on pigs, and Boston Children's Hospital researchers are hopeful it could be used for humans with heart failure in the not-distant future. Crucially, it could be used for pediatric patients with congenital conditions, who often only have an issue on one side of their heart, so an invasive pump insertion is overkill.
When we talk about artificial intelligence in medicine, we often debate whether machines will replace tasks doctors do today. A more tantalizing possibility is performing tasks doctors can't--using large data sets, and modern computational tools like deep learning, to recognize patterns too subtle for any human to discern. Today, we're presenting early clinical results showing Cardiogram's deep neural network, DeepHeart, can do just that: recognize hypertension and sleep apnea from wearable heart rate sensors with 82% and 90% accuracy, respectively . The American Heart Association is highlighting this work, conducted in partnership with the UC San Francisco's Health eHeart Study, as one of three Best Abstracts in Health Tech at their annual AHA Scientific Sessions, a meeting of roughly 18,000 cardiologists. Globally, 1.1 billion people have hypertension (chronic high blood pressure) and 1 in 5 are undiagnosed.
Since heart disease is a primary killer of human beings around the world, it's no surprise that effort and focus from many AI innovators is on heart disease diagnosis and prevention. The current process to determine an individual's risk factor for a heart attack is to look at the American College of Cardiology/American Heart Association's (ACC/AHA) list of risk factors that include age, blood pressure and more. However, this is really a simplistic approach and doesn't take into account medications someone might be on, the health of the patient's other biological systems and other factors that could increase odds of a heart ailment. Several research teams, including those at Carnegie Mellon University and a study from Stephen Weng and his associates at University of Nottingham in the United Kingdom, are working toward enhancing machine learning so algorithms will be able to predict (better than humans) who is at risk and when they might be at risk for a heart attack. Preliminary results of the AI algorithms were significantly better at predicting heart attacks than the ACC/AHA guidelines.
The Apple Watch can accurately detect sleep apnea and high blood pressure, a study found. An app called Cardiogram implemented technology earlier this year to detect abnormal heart rhythm used to predict and prevent heart disease by tracking heart rate. Now new research has found the technology can detect other serious conditions such as sleep apnea with 90 percent accuracy and hypertension with 82 percent accuracy. Being able to detect these conditions through wearing a simple device that continuously screens the body's heart rate will cut the time between doctors visits, tests and lead to a quicker diagnosis. The study was conducted using the Cardiogram app with more than 6,000 participants wearing the Apple Watch.
Researchers studying the healthcare implications for modern wearable devices have found that wrist-worn gadgets like Apple Watch and Fitbit can be used to accurately detect hypertension and sleep apnea. The research claims that data from wearable heart rate sensors, when combined with machine learning algorithms, can surface hidden patterns that predict whether a person is at risk for certain health problems. The study was conducted by health startup Cardiogram and UCSF and followed more than 6,000 subjects, some of whom had diagnosed hypertension and sleep apnea. More broadly, the research aims to spur business development around the use of wearables within preventive medicine. "What if we could transform wearables people already own -- Apple Watches, Android Wears, Garmins, and Fitbits -- into inexpensive, everyday screening tools using artificial intelligence?"
The Apple Watch may be a useful tool for detecting potentially fatal medical conditions such as hypertension (high blood pressure) and sleep apnea (a sleep disorder characterized by pauses in breathing while a person is asleep), claims a new study. It follows a similar previous study by Cardiogram and UCSF, which demonstrated that the Apple Watch can detect abnormal heart rhythms with a 97 percent accuracy. The new study involved 6,115 participants, who were recruited through the Cardiogram app to test using their Apple Watch. The researchers then used a deep learning artificial neural network -- one of the tools which is at the centre of recent big advances in artificial intelligence -- to carry out diagnosis based on users' heart rate and step count data. It required no additional hardware, other than what the Apple Watch already features.
Your Apple Watch can tell if you have hypertension or sleep apnea -- with the help of Cardiogram's deep neural network, DeepHeart, that is. The app-maker and the UCSF Health lab have conducted a study proving that wearables can suggest the presence of hypertension and sleep apnea with 82 percent and 90 percent accuracy, so long as they come equipped with heart rate sensors and accelerometers. And, yes, they're not just talking about Apple Watch, but also Android Wear devices, Garmins and Fitbits. According to Cardiogram, heart rate sensors can detect both conditions, because your body's autonomic nervous system connects your heart with the brain, stomach, esophagus, liver, intestines, pancreas and blood vessels. The company needed data to be able to train its AI to recognize heart rate patterns that denote the presence of the conditions, though, so it recruited 6,115 of its app's users to participate in an online study with the UCSF Health lab.
The world's most valuable company crammed a lot into the tablespoon-sized volume of an Apple Watch. There's GPS, a heart-rate sensor, cellular connectivity, and computing resources that not long ago would have filled a desk-dwelling beige box. The wonder gadget doesn't have a sphygmomanometer for measuring blood pressure or polysomnographic equipment found in a sleep lab--but thanks to machine learning, it might be able to help with their work. Research presented at the American Heart Association meeting in Anaheim Monday claims that when paired with the right machine-learning algorithms, the Apple Watch's heart-rate sensor and step counter can make a fair prediction of whether a person has high blood pressure, or sleep apnea, in which breathing stops and starts repeatedly through the night. Both are common--and commonly undiagnosed--conditions associated with life-threatening problems, including stroke and heart attack.
What's the first thing that comes to mind when you think of a robot? Well yes, you're not wrong, however, I want to introduce a new breed of robots that might change the way you think about them entirely. 'Soft robots', unlike their old, rigid counterparts, are simply robots constructed with soft materials. These transformations have emerged due to recent new fields of science such as biomimetics (looking to nature to solve complex human problems) and morphological computation (replicating physical systems to improve computation efficiency). In other words, systems that basically mimic nature and biology to make things work more efficiently.
New York University's Langone Medical Center is developing one AI system to predict which patients are likely to develop the dangerous condition sepsis and another that alerts doctors to cases of heart trouble. "If you're admitted to the ER for pneumonia, the people who are treating you may not think about the fact that you also have congestive heart failure," says Michael Cantor, an internist and associate professor in the hospital's departments of population health and medicine. The system will go through each patient's record when they're admitted and automatically alert cardiologists to anyone who has heart failure, so they can advise on how to avoid treatments that might exacerbate that condition.