Therapeutic Area


Chinese pre-schools use robots to do daily health checks of children

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The school nurse of the future could be a robot if Chinese technology catches on – but British people may be too suspicious, experts say. Children at more than 2,000 pre-schools in the Asian country now have their health checked every morning by a machine. The Walklake robot, which has a square body and cartoon-like face, takes just three seconds to scan a child's hands, eyes, and throats. And if it picks up any signs of illness – red eyes, rashes or mouth ulcers, for example – it can refer the child to a human nurse. One British doctor said he thought parents in the UK wouldn't want the technology and it could disrupt children's learning, but another called it'a great idea'.


Chinese pre-schools use robots to do daily health checks of children

#artificialintelligence

The school nurse of the future could be a robot if Chinese technology catches on – but British people may be too suspicious, experts say. Children at more than 2,000 pre-schools in the Asian country now have their health checked every morning by a machine. The Walklake robot, which has a square body and cartoon-like face, takes just three seconds to scan a child's hands, eyes, and throats. And if it picks up any signs of illness – red eyes, rashes or mouth ulcers, for example – it can refer the child to a human nurse. One British doctor said he thought parents in the UK wouldn't want the technology and it could disrupt children's learning, but another called it'a great idea'.


AI Surpasses Neuroscientists in Spotting Neurons

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Ever wonder how neuroscientists are able to monitor and study what happens inside a living brain in action? One of the challenges in neuroscience is observing the activity of neurons intact in brain tissue that is taking place in a living organism--in vivo. One approach, two-photon calcium imaging, is a method developed circa 1990. In mammalian neurons, calcium is an intracellular messenger. This imaging approach involves the loading of calcium ions (Ca2) indicator dyes in the desired brain region for neuronal monitoring and a two-photon laser scanning microscope for visualization.


Dude, Where's My Frontal Cortex? - Issue 72: Quandary

Nautilus

In the foothills of the Sierra Mountains, a few hours east of San Francisco, are the Moaning Caverns, a cave system that begins, after a narrow, twisting descent of 30-some feet, with an abrupt 180-foot drop. The Park Service has found ancient human skeletons at the bottom of the drop. Instead, these explorers took one step too far in the gloom. The skeletons belonged to adolescents. After all, adolescence is the time of life when someone is most likely to join a cult, kill, be killed, invent an art form, help overthrow a dictator, ethnically cleanse a village, care for the needy, transform physics, adopt a hideous fashion style, commit to God, and be convinced that all the forces of history have converged to make this moment the most consequential ever, fraught with peril and promise. For all this we can thank the teenage brain. Some have argued adolescence is a cultural construct. In traditional cultures, there is typically a single qualitative transition to puberty. After that, the individual is a young adult.


Machine learning could help make antibiotics more effective

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Jason Yang, an IMES research scientist, is the lead author of the paper, which appears in the May 9 issue of Cell. Other authors include Sarah Wright, a recent MIT MEng recipient; Meagan Hamblin, a former Broad Institute research technician; Miguel Alcantar, an MIT graduate student; Allison Lopatkin, an IMES postdoc; Douglas McCloskey and Lars Schrubbers of the Novo Nordisk Foundation Center for Biosustainability; Sangeeta Satish and Amir Nili, both recent graduates of Boston University; Bernhard Palsson, a professor of bioengineering at the University of California at San Diego; and Graham Walker, an MIT professor of biology.


Automated Inspiration

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In the 19th century, doctors might have prescribed mercury for mood swings and arsenic for asthma. It might not have occurred to them to wash their hands before your surgery. They weren't trying to kill you, of course--they just didn't know any better. These early doctors had valuable data scribbled in their notebooks, but each held only one piece in a grand jigsaw puzzle. Without modern tools for sharing and analyzing information--as well as a science for making sense of that data--there wasn't much to stop superstition from influencing what could be seen through a keyhole of observable facts.


Robots conduct daily health inspections of schoolchildren in China

New Scientist

Please stand in front of Walklake for your examination. This health checking robot takes just 3 seconds to diagnose a variety of ailments in children, including conjunctivitis, and hand, foot and mouth disease. Over 2000 preschools in China, with children aged between 2 and 6, are using Walklake every morning to check the health status of their students. Walklake has a boxy body and smiling cartoony face.


An AI Pioneer Explains the Evolution of Neural Networks

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Geoffrey Hinton is one of the creators of Deep Learning, a 2019 winner of the Turing Award, and an engineering fellow at Google. Last week, at the company's I/O developer conference, we discussed his early fascination with the brain, and the possibility that computers could be modeled after its neural structure--an idea long dismissed by other scholars as foolhardy. We also discussed consciousness, his future plans, and whether computers should be taught to dream. The conversation has been lightly edited for length and clarity. Nicholas Thompson: Let's start when you write some of your early, very influential papers. Everybody says, "This is a smart idea, but we're not actually going to be able to design computers this way." Explain why you persisted and why you were so confident that you had found something important. Geoffrey Hinton: It seemed to me there's no other way the brain could work. It has to work by learning the strength of connections. And if you want to make a device do something intelligent, you've got two options: You can program it, or it can learn. And people certainly weren't programmed, so we had to learn. This had to be the right way to go. NT: Explain what neural networks are. GH: You have relatively simple processing elements that are very loosely models of neurons. They have connections coming in, each connection has a weight on it, and that weight can be changed through learning. And what a neuron does is take the activities on the connections times the weights, adds them all up, and then decides whether to send an output.


Predicting failures of Molteno and Baerveldt glaucoma drainage devices using machine learning models

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The purpose of this retrospective study is to measure machine learning models' ability to predict glaucoma drainage device failure based on demographic information and preoperative measurements. The medical records of sixty-two patients were used. Potential predictors included the patient's race, age, sex, preoperative intraocular pressure, preoperative visual acuity, number of intraocular pressure-lowering medications, and number and type of previous ophthalmic surgeries. Failure was defined as final intraocular pressure greater than 18 mm Hg, reduction in intraocular pressure less than 20% from baseline, or need for reoperation unrelated to normal implant maintenance. Five classifiers were compared: logistic regression, artificial neural network, random forest, decision tree, and support vector machine.


Google's AI boosts accuracy of lung cancer diagnosis, study shows - STAT

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One of lung cancer's most lethal attributes is its ability to trick radiologists. Some nodules appear threatening but turn out to be false positives. Others escape notice entirely, and then spiral without symptoms into metastatic disease. On Monday, however, Google unveiled an artificial intelligence system that -- in early testing -- demonstrated a remarkable talent for seeing through lung cancer's disguises. A study published in Nature Medicine reported that the algorithm, trained on 42,000 patient CT scans taken during a National Institutes of Health clinical trial, outperformed six radiologists in determining whether patients had cancer.