A project by students at Carnegie Mellon could save lives. Called the HopeBand, the wristband senses low blood oxygen levels and sends a text message and sounds an alarm if danger is imminent.
“Imagine having a friend who is always watching for signs of overdose; someone who understands your usage pattern and knows when to contact [someone] for help and make sure you get help,” student Rashmi Kalkunte told IEEE. “That’s what the HopeBand is designed to do.”
The team won third place in the Robert Wood Johnson Foundation’s Opioid Challenge at the Health 2.0 conference in September and they are planning to send the band to a needle exchange program in Pittsburgh. They hope to sell it for less than $20.
Given the more than 72,000 overdose deaths in America this year a device like this could definitely keep folks a little safer.
The Ebola outbreak in the Democratic Republic of the Congo has spread to a city of nearly 1 million residents. There are now 30 confirmed cases and 15 deaths in the city of Butembo reported in the latest update provided by the World Health Organization (WHO). The number of cases in the city center is still low, according to Doctors Without Borders, but that number is rising quickly in more outlying districts and suburbs.
The outbreak, which has been going on since August, has so far resulted in 467 confirmed cases and a further 48 probable cases. More than half of the cases have resulted in death (including those of 17 health workers), while 177 patients have recovered, including a newborn baby.
The rate of transmission is beginning to slow down in Beni, a smaller city approximately 36 miles north of Butembo that has the highest number of reported cases so far. But “the outbreak is intensifying in Butembo and Katwa,” writes the WHO, “and new clusters are emerging elsewhere.”
When Apple introduced the fourth iteration of its smartwatch, the big new selling point wasn’t a feature we typically associate with a watch or any sort of smart device. Instead, the company added a feature that had only recently arrived in the form of specialized consumer devices: an electrocardiograph (ECG), a device made for monitoring the heart’s electrical activity.
But the watch was ready before the software was, meaning an examination of the technology wasn’t possible in our comprehensive review of the Apple Watch Series 4. Last week, Apple finally enabled the missing features, and we’ve spent a few days checking them out.
People who haven’t used the Apple Watch may not realize just how much it’s an extension of an iPhone. This includes the heart-monitoring software, which requires an update to both the Watch and iPhone OSes before it will work. (This caused a small bit of confusion when the software wouldn’t launch after we upgraded only the watch’s OS.) Once the update is done, the Health app on the iPhone will incorporate any ECG data generated using the watch. On the watch side, the update will install a new app.
The placenta supports the fetus while it is in utero (and, according to some, a placenta can even support a rosebush if it’s buried under one after delivery). We know a placenta is essential for a successful pregnancy, but we don’t really know exactly how it works because we’ve had no experimental models we can use to study it. Until now.
Researchers in England examined all of the signaling molecules they could find rushing around between the mother and the placenta (which originates from fetal tissue), figuring they might induce the placenta to grow and develop. From this analysis they generated a blend of signaling molecules that they expected could induce placental formation in a culture dish.
They then obtained cells from first trimester placentas from women who had had normal pregnancies but decided to abort. These cells were grown in media that contained the factors identified by this team. Within a week, the cells were growing into organoids—small blobs of tissue similar to organs in mature organisms. The cells seem perfectly happy in this media; at the time this paper was written, they have been stable for a year.
In 2010, the World Health Organization (WHO) set some ambitious goals for measles worldwide. By 2015, they wanted to reduce the number of deaths caused by measles by 95 percent compared to 2000. They set similarly ambitious targets for vaccination rates and measles infections.
The world has not reached these goals. And between 2016 and 2017, there was an alarming uptick in measles cases worldwide, according to a joint report by the WHO and CDC. “Complacency about the disease and the spread of falsehoods about the vaccine in Europe, a collapsing health system in Venezuela, and pockets of fragility and low immunization coverage in Africa are combining to bring about a global resurgence of measles after years of progress,” said Dr Seth Berkley, CEO of Gavi, the Vaccine Alliance, in a statement about the report.
Overall, between 2000 and 2017, there has been a lot of progress: annual global deaths have decreased 80 percent, from 545,174 to 109,638. Over this time period, measles vaccination has prevented approximately 21 million deaths globally, compared to a hypothetical world with no measles vaccines (in this world, the death rate would have been a lot higher in 2000, too). The number of cases reported annually plummeted from 145 cases per million people to just 25—although the goal was five cases per million. And 85 percent of people globally had received the first dose of the measles vaccine in 2017, compared to 72 percent in 2000.
Until recently, humans have relied on the trained eyes of doctors to diagnose diseases from medical images.
Beijing-based Infervision is among a handful of artificial intelligence startups around the world racing to improve medical imaging analysis through deep learning, the same technology that powers face recognition and autonomous driving.
The startup, which has to date raised $70 million from leading investors like Sequoia Capital China, began by picking out cancerous lung cells, a prevalent cause of death in China. At the Radiological Society of North America’s annual conference in Chicago this week, the three-year-old company announced extending its computer vision prowess to other chest-related conditions like cardiac calcification.
“By adding more scenarios under which our AI works, we are able to offer more help to doctors,” Chen Kuan, founder and chief executive officer of Infervision, told TechCrunch. While a doctor can spot dozens of diseases from one single image scan, AI needs to be taught how to identify multiple target objects in one go.
But Chen says machines already outstrip humans in other aspects. For one, they are much faster readers. It normally takes doctors 15 to 20 minutes to scrutinize one image, whereas Infervision’s AI can process the visuals and put together a report under 30 seconds.
AI also addresses the longstanding issue of misdiagnosis. Chinese clinical newspaper Medical Weekly reported that doctors with less than five years’ experience only got their answers right 44 percent of the time when diagnosing black lung, a disease common among coal miners. And research from Zhejiang University that examined autopsies between 1950 to 2009 found that the total clinical misdiagnosis rate averaged 46 percent.
“Doctors work long hours and are constantly under tremendous stress, which can lead to errors,” suggested Chen.
The founder claimed that his company is able to improve the accuracy rate by 20 percent. AI can also fill in for doctors in remote hinterlands where healthcare provision falls short, which is often the case in China.
Winning the first client
A report on bone fractures produced by Infervision’s medical imaging tool
Like any deep learning company, Infervision needs to keep training its algorithms with data from varied sources. As of this week, the startup is working with 280 hospitals — among which 20 are outside of China — and steadily adding a dozen new partners weekly. It also claims that 70 percent of China’s top-tier hospitals use its lung-specific AI tool.
But the firm has had a rough start.
Chen, a native of Shenzhen in south China, founded Infervision after dropping out of his doctoral program at the University of Chicago where he studied under Nobel-winning economist James Heckman. For the first six months of his entrepreneurial journey, Chen knocked on the doors of 40 hospitals across China — to no avail.
“Medical AI was still a novelty then. Hospitals are by nature conservative because they have to protect patients, which make them reluctant to partner with outsiders,” Chen recalled.
Eventually, Sichuan Provincial People’s Hospital gave Infervision a shot. Chen with his two founding members got hold of a small batch of image data, moved into a tiny apartment next to the hospital, and got the company underway.
“We observed how doctors work, explained to them how AI works, listened to their complaints, and iterated our product,” said Chen. Infervision’s product proved adept, and its name soon gathered steam among more healthcare professionals.
“Hospitals are risk-averse, but as soon as one of them likes us, it goes out to spread the word and other hospitals will soon find us. The medical industry is very tight-knit,” the founder said.
It also helps that AI has evolved from a fringe invention to a norm in healthcare over the past few years, and hospitals start actively seeking help from tech startups.
Infervision has stumbled in its foreign markets as well. In the U.S., for example, Infervision is restricted to visiting doctors only upon appointments, which slows product iteration.
Chen also admitted that many western hospitals did not trust that a Chinese startup could provide state-of-the-art technology. But they welcomed Infervision in as soon as they found out what it’s able to achieve, which is in part thanks to its data treasure — up to 26,000 images a day.
“Regardless of their technological capability, Chinese startups are blessed with access to mountains of data that no startups elsewhere in the world could match. That’s an immediate advantage,” said Chen.
There’s no lack of rivalry in China’s massive medical industry. Yitu, a pivotal player that also applies its AI to surveillance and fintech, unveiled a cancer detection tool at the Chicago radiological conference this week.
Infervision, which generates revenues by charging fees for its AI solution as a service, says that down the road, it will prioritize product development for conditions that incur higher social costs, such as cerebrovascular and cardiovascular diseases.
As more details regarding the first gene-edited humans are released, things continue to look worse. The researcher who claimed the advance, He Jiankui, has now given a public talk that includes many details on the changes made at the DNA level. The details make a couple of things clear: we don’t know whether the editing will protect the two children from HIV infections, and we can’t tell whether any areas of the genome have been damaged by the procedure.
All of that raises even further questions as to whether He followed ethical guidelines when performing the work and getting consent from the parents. And, more generally, nobody is sure why He chose to ignore a strong consensus that the procedure wasn’t yet ready for use in humans. In response to the outcry, the Chinese government has shut down all further research by He, even as it was revealed that a third gene-edited baby may be on the way.
While the US already has rules in place that are intended to keep research like He’s from happening, a legal scholar Ars spoke with suggested there may be a loophole that could allow something similar here. In light of that, it’s important to understand the big picture He has potentially altered. What exactly happened in China and why does it concern so many in the scientific community?
In 1900, the average person in the US could expect to live just 47.3 years. Throughout the 20th century, that figure climbed rapidly, topping 70 years for the first time in 1961 and reaching 78.9 years in 2014, suggesting 80 was only a matter of time.
Then in 2015, there was a downturn—a small one, to 78.8 years. A single year might be a blip, but the reasons for the increase in death rate (including obesity and drug overdoses) suggested that might not be the case. Data released today by the CDC’s National Center for Health Statistics points to a continuing downward trend: life expectancy in 2017 was 78.6 years, down from 78.7 years in 2016.
That dip of 0.1 years, every year for the last three years, is not a huge trend when taken on its own. But it suggests that the decrease in 2015 was more than a blip—and it points to unfolding stories about health and death in the United States. Those small-seeming numbers also translate to meaningful real-world figures: there were 69,255 additional deaths in 2017 compared to 2016.