Healthcare is a multitrillion-dollar global industry, growing each year as average life expectancy rises — and with nearly unlimited facets and sub-specialties.

For medical professionals, new technologies can change the way they work, enable more accurate diagnoses and improve care. For patients, healthcare innovations lessen suffering and save lives.

Deep learning can be implemented at every stage of healthcare, creating tools that doctors and patients can take advantage of to raise the standard of care and quality of life.

How AI Is Changing Patient Care

Providing patient care is a series of critical choices, from decisions made on a 911 call to the recommendations a primary physician makes at an annual physical. The challenge is getting the right treatments to patients as fast and efficiently as possible.

Nearly half the countries and territories in the world have less than one physician per 1,000 people, a third of the threshold value to deliver quality healthcare, according to a 2018 study in The Lancet. Meanwhile, as healthcare data goes digital, the amount of information medical providers collect and refer to is growing.

In intensive care units, these factors come together in a perfect storm — patients who need round-the-clock attention; large, continuous data feeds to interpret; and a crucial need for fast, accurate decisions.

Researchers at MIT’s Computer Science and Artificial Intelligence Lab developed a deep learning tool called ICU Intervene, which uses hourly vital sign measurements to predict eight hours in advance whether patients will need treatments to help them breathe, require blood transfusions or need interventions to improve heart function.

Corti, a Denmark-based startup, is stepping in at another time-sensitive interaction: phone calls with emergency services. The company is using an NVIDIA Jetson TX2 module to analyze emergency call audio and help dispatchers identify cardiac arrest cases in under a minute.

LexiconAI, a member of the NVIDIA Inception program, is helping doctors spend more time with their patients every day. The startup built a mobile app that uses speech recognition to capture medical information from doctor-patient conversations — making it possible to automatically fill in electronic health records.

How AI Is Changing Pathology

Just as millions of medical scans are taken each year, so too are hundreds of millions of tissue biopsies. While pathologists have long used physical slides to analyze specimens and make diagnoses, these slides are increasingly being scanned to create digital pathology datasets.

Inception startup Proscia uses deep learning to analyze these digital slides, scoring over 99 percent accuracy for classifying three common skin pathologies. Using AI can help standardize diagnoses, which is important. Depending on the type and stage of disease, two pathologists looking at the same tissue may disagree on a diagnosis more than half the time.

SigTuple, another Inception startup, developed an AI microscope to analyze blood and bodily fluids. The microscope scans physical slides under a lens and uses GPU-accelerated deep learning to analyze the digital images either on SigTuple’s AI platform in the cloud or on the microscope itself.

Compared to scanners that automatically convert glass slides to digital images and interpret the results, SigTuple’s microscope does this at a fraction of the cost. The company hopes its tool will address the global pathologist shortage, a crucial problem in many countries.

How AI Is Changing Predictive Health

A host of AI tools are being developed to detect risk factors for diseases months before symptoms appear. These will help doctors make earlier diagnoses, conduct longevity studies or take preventative action. Taking advantage of the ability of deep learning models to spot patterns in large datasets, these tools may extract insights from electronic health records, physical features or genetic information.

One mobile app, Face2Gene, uses facial recognition and AI to identify about 50 known genetic syndromes from photos of patients’ faces. It’s used by around 70 percent of geneticists worldwide and could help cut down the time it takes to get an accurate diagnosis.

Another deep learning tool, developed by researchers at NYU, analyzes lab tests, X-rays and doctors notes to predict ailments like heart failure, severe kidney disease and liver problems three months faster than traditional methods.

Using AI and a wide range of electronic health records helped the researchers draw new connections among hundreds of health measurements that could predict diseases like diabetes.

How AI Is Enabling Healthcare Apps

Healthcare doesn’t start and end at the doctor’s office. And with wearables, smartphones and IoT devices, there’s no shortage of devices to monitor health from anywhere.

A service called SpiroCall, for example, makes it possible for patients to check lung function by breathing into a smartphone, either by dialing a toll-free number or recording a sound file on an app. The data is sent to a central server, which uses a deep learning model to assess lung health.

For athletes at risk of suffering concussions on the playing field, an AI-powered app is using a smartphone camera to analyze how an athlete’s pupils respond to light, a metric medical professionals use to diagnose brain injury.

And in the realm of mental health, Canadian startup Aifred Health is using GPU-accelerated deep learning to better tailor depression treatments to individual patients. Using data on a patient’s symptoms, demographics and medical test results, the neural network helps doctors as they prescribe treatments.

How AI Is Enabling Devices for People with Disabilities

A billion people around the world experience some form of disability. AI-powered technology can provide some of them with a greater level of independence, making it easier to perform daily tasks or get around.

Aira, a member of the Inception program, has created an AI platform that connects to smart glasses, helping people with impaired vision with tasks like reading labels on medication bottles. And a professor at Ohio State University is using GPUs and deep learning to create a hearing aid that can bump the volume of speech while filtering out background noise.

Researchers at OSU and Battelle, a nonprofit research organization, are developing a brain-computer interface powered by neural networks that can read thoughts and restore movement to paralyzed limbs.

And a team at Georgia Tech developed an AI prosthetic hand that helped jazz musician Jason Barnes play piano for the first time in five years. The prosthesis uses electromyogram sensors to recognize muscle movement and allows for individual finger control.

See the NVIDIA healthcare page for more.

Main image licensed from iStock.

The post How AI Is Transforming Healthcare appeared first on The Official NVIDIA Blog.

Radiology. Autonomous vehicles. Supercomputing. The changes sweeping through all these fields are closely related. Just ask NVIDIA CEO Jensen Huang.

Speaking in Boston at the World Medical Innovation Forum to more than 1,800 of the world’s top medical professionals, Huang tied Monday’s news — that NVIDIA is collaborating with the American College of Radiology to bring AI to thousands of hospitals and imaging centers — to the changes sweeping through fields as diverse as autonomous vehicles and scientific research.

In a conversation with Keith Dryer, vice chairman of radiology at Massachusetts General Hospital, Huang asserted that data science — driven by a torrent of data, new algorithms and advances in computing power — is becoming a fourth pillar of scientific discovery, alongside theoretical work, experimentation and simulation.

Putting data science to work, however, will require enterprises of all kinds to learn how to handle data in new ways. In the case of radiology, the privacy of the data is too important, and the expertise is local,  Huang told the audience. “You want to put computing at the edge,” he said.

As a result, the collaboration between NVIDIA and the American College of Radiology promises to enable thousands of radiologists nationwide to use AI for diagnostic radiology in their own facilities, using their own data, to meet their own clinical needs.

Huang began the conversation by noting that the Turing Award, “the Nobel Prize of computing,” had just been given to the three researchers who kicked off today’s AI boom: Yoshua Bengio, Geoffrey Hinton and Yann LeCunn.

“The takeaway from that is that this is probably not a fad, that deep learning and this data-driven approach where software and the computer is writing software by itself, that this form of AI is going to have a profound impact,” Huang said.

#WMIF19 @nvidia Using AI to train AI, AI tools to annotate in radiology, Software is automation and AI is the automation of automation @PHSInnovation pic.twitter.com/4Y2YDpprGQ

— Bharti Khurana, MD (@KhuranaBharti) April 8, 2019

Huang drew parallels between radiology and other industries putting AI to work, such as automotive, where Huang sees an enormous need for computing power in autonomous vehicles that can put multiple intelligences to work, in real time, as they travel through the world.

Similarly, in medicine, putting one — or more — AI models to work will only enhance the capabilities of the humans guiding these models.

These models can also guide those doing cutting-edge work at the frontiers of science, Huang said, citing Monday’s announcement that the Accelerating Therapeutics for Opportunities in Medicine, or ATOM, consortium will collaborate with NVIDIA to scale ATOM’s AI-driven drug discovery program.

The big idea: to pair data science with more traditional scientific methods, using neural networks to help “filter” through the large combination of possible molecules to decide which ones to simulate to find candidates for in vitro testing, Huang explained

Yes! Excited that at #WMIF19 Jensen huang @nvidia CEO was talking about partial differential equations and first principals related to #ai and got into some of the #math at a top level. It warmed my #mechanicalengineer PhD nerd heart.

— Brian Mullen (@mullen_brian) April 8, 2019

Software Is automation, AI Is the Automation of Automation

Huang sees such techniques being used in all fields of human endeavor — from science to front-line healthcare and even to running a technology company. As part of that process, NVIDIA has built one of the world’s largest supercomputers, SATURNV, to support its own efforts to train

AI models with a broad array of capabilities. “We use this for designing chips, for improving our systems, for computer graphics,” Huang said.

Such techniques promise to revolutionize every field of human endeavor, Huang said, asserting that AI is “software that writes software,” and that software’s “fundamental purpose is automation.”

“AI therefore is the automation of automation,” Huang said. “And if we can harness the automation of automation, imagine what good we could do.”

The post NVIDIA CEO Ties AI-Driven Medical Advances to Data-Driven Leaps in Every Industry appeared first on The Official NVIDIA Blog.

Food is too valuable to waste.

But nearly $100 billion of it is thrown away in the hospitality sector every year.

When you’re catering for an unknown number of guests, you can’t afford to be underprepared. In many cases, this can lead kitchen staff to the other extreme — preparing too many meals. All of the extra, unused ingredients ultimately end up in the bin.

Winnow, a U.K.-based company, is using AI to take a bite out of food waste by empowering commercial kitchens to reduce the amount of food they dump.

AI for Reducing Food Waste

Around one-third of the food produced globally for human consumption is wasted every year. That amounts to a staggering 1.3 billion tonnes.

Winnow is helping professional chefs curb those numbers with its latest product, Winnow Vision, which automatically detects, identifies and measures food at the point it is thrown out.

The system involves a set of digital weighing scales on top of which sits a standard kitchen bin. Mounted above this is a camera and compute system containing an NVIDIA Jetson TX2 supercomputer on a module.

The module takes the images captured by the camera, as well as the weight recorded by the scales, and determines what is being thrown out and in what quantity. The neural networks used by the Jetson TX2 are trained using AWS instances with NVIDIA V100 GPUs on TensorFlow. To identify the wide variety of food the system may encounter, a huge amount of training data is needed — up to 1,000 images per food item.

The collected data is sent to the cloud for processing and regular reports are then created and shared with kitchen staff. The reports detail quantities and types of food being tossed, as well as recommendations as to how the kitchen can reduce waste.

Winnow co-founder and CEO Marc Zornes explains why the real-time deep learning results the Jetson TX2 delivers onsite — what’s known as “inference at the edge” — are key.

“It’s really important to us that the customer receives immediate results, in an environment that cannot guarantee a reliable and fast internet connection,” said Zornes. “Using the Jetson TX2 devices in the field enables us to provide, in real time, a ‘better than human’ understanding of what is being thrown into the bin on the edge, live, in the kitchen.”

The Jetson TX2 module can run multiple processes. Having a complete system on the edge means the Winnow team can reuse knowledge gained from working in the cloud and apply it to an edge paradigm. The Jetson platform is powerful enough to encompass current and future workloads, and flexible enough for Winnow to experiment and design new solutions.

Business Sense

Winnow Vision has already surpassed human levels with an accuracy rate of over 80 percent when identifying food that has ended up in the trash. This will increase with time as more and more data is collected.

The system is already installed in over 75 kitchens and Winnow plans to roll out the technology to thousands more in the coming years. IKEA and Emaar are among the companies that have implemented Winnow Vision in their kitchens.

Reducing the amount of food waste isn’t the only benefit for businesses. Automating the process increases efficiency in the kitchen, too. Staff require less training on food management and need to spend less time adjusting their menus.

Winnow has shown that by arming teams with analytics, food waste can be cut in half. The company estimates it has already helped commercial kitchens save more than $30 million in annualized food costs. That equates to preventing over 23 million meals from going in the trash.

With the advent of its new technology, Winnow has announced that it aims to save kitchens $1 billion by 2025.

The post Wasting Away: Winnow Slims Down Commercial Food Waste appeared first on The Official NVIDIA Blog.