The farm-to-fork movement is getting a taste of AI.

Startup OneSoil cultivates AI to help farmers boost their bounty. The company offers a GPU-enabled platform that turns satellite data into farm analytics for soil and crop conditions.

The Belarus-based company interprets satellite feeds to show how plants reflect different light waves, and it rates the state of plant growth based off this information for plots of land.

OneSoil’s free platform displays how areas of land measure up on the standard known as the NDVI (Normalized Difference Vegetation Index). Farmers can use this vegetation score to spot unhealthy crop areas that need inspection and to plan watering needs and the application of fertilizers.

The field monitoring platform is available as an Android app and on the web.

OneSoil has developed its platform to cover North America, most of western Europe and some of central Europe.  It aims to have coverage of the entire world by year’s end. The satellite data visualizations are updated every three to five days.

Satellite to Sprouts

OneSoil taps into free satellite data from the European Union’s Copernicus Earth observation program. The company manually marked out boundaries on nearly 400,000 fields for training data used on its convolutional neural networks. Now its algorithms can now enable its algorithms to automatically create boundaries from the satellite data.

It processed about 50 terabytes of Sentinel 2 satellite data using NVIDIA GPUs in Microsoft Azure to build out its boundaries of land for the map spanning much of the world.

“With Sentinel images, we need a lot of processing power to analyze those,” said Clement Matyuhov, director of business development at OneSoil.

OneSoil can automatically detect more than 20 different crop types.

Dig the Sensors

OneSoil has developed sensors to work on its platform. Customer can dig a hole and stick in one of its battery-powered sensors that packs a SIM card to start sending data.

The sensors measure air humidity, soil moisture, the temperature of air and soil, and the level of light intensity for the nearby area.

The company has also developed a modem that can transfer data between agricultural equipment and the OneSoil platform over a mobile network.

OneSoil users can enter data, as well. They can make such entries as date of harvest, crop type, average yield, field boundaries and files documenting field work. They can use the app, which tracks location and provides field data, to go examine areas.

Prescriptive Agriculture

On the analytics side, OneSoil Maps makes it easy for farmers to make adjustments on their land. The maps provide a productivity rating of low, medium or high for different areas of the land.

“We can say there is a low productivity zone there, so go check it out. Within one field, the productivity can vary dramatically,” said Matyuhov.

Farmers can use the maps for the vegetation on their land to create prescription maps for fertilizer. These prescription maps, downloadable as a file from app.onesoil.ai, can be uploaded into compatible tractors from John Deere and steering systems from Trimble, allowing tractors to go to the specific GPS coordinates and treat the area as prescribed.

“It’s really an expert assessment for the farmer. The results for the yield can be substantial,” he

Image and credit: Corn harvest with an IHC International combine harvester, Jones County, Iowa, U.S., by Bill Whittaker under Creative Commons license.

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Radiologists are tasked with diagnosing some of the most serious medical conditions — but their workloads are becoming increasingly demanding as the volume of imaging studies such as CT and MRI has steadily gone up.

Houston-based InformAI is stepping in to help reduce fatigue and stress for radiologists by building deep learning tools that can help them analyze medical scans faster.

“We wanted to build diagnostic-assist tools for clinicians to speed up information workflow and decision-making at the point of care to benefit patients,” said InformAI CEO Jim Havelka.

InformAI trains its deep learning image classifiers and patient outcome predictors on NVIDIA V100 GPUs through the Microsoft Azure cloud platform and with an onsite NVIDIA DGX Station. The startup worked with data science consulting firm SFL Scientific to develop a convolutional neural network-based deep learning technology stack using top technology resources.

In less than 30 seconds, InformAI’s image classifier scans for 20 sinus conditions and flags which ones might be present in a patient’s 3D CT scan. This AI tool has also formed the basis for other image classification applications that analyze 3D scans of soft tissue — including detecting common brain cancers from MRI scans.

AI Spots Sinus Conditions

Figuring out the structure of an individual’s sinuses is harder than it sounds. Each person’s sinus cavities look different, making it challenging for AI to determine if an infection or abnormal mass is present in the eight major sinus cavities and passageways that connect them.

Doctors perform around 700,000 sinus procedures each year in the United States. Using AI to speed up the diagnostics workflow can save on healthcare costs and shorten the time it takes to begin treatment.

InformAI and its healthcare partners built a training dataset was built consisting of approximately 6 million images from 20,000 patient studies. The scans were labeled by a team of radiologists and medical residents who worked with the company on the project.

Radiologists using the startup’s platform can examine and analyze 3D sinus CT scans while the predictor neural network is running. In under a minute, the AI results pop up for 20 sinus medical conditions, which the doctors can then use to assist in their diagnosis and treatment planning process.

InformAI is deploying the sinus classifier this spring at a hospital and several clinics to test its effectiveness as an assist tool for radiologists and ear, nose and throat physicians. The team is also going through the regulatory process required for the AI to be certified as a direct diagnostic tool.

A Neural Network for Neurological Disorders

In general terms, the sinus classification neural network extracts 3D segments from a CT scan to analyze whether a particular disease or set of diseases is present in those image segments, Havelka said. Since the network was trained on such a large medical dataset, it can be repurposed using transfer learning to solve image classification problems for a broad range of soft tissue medical applications.

The startup is doing just that. Using transfer learning, the team trained a neural network to detect disease from another kind of soft tissue: the brain.

When a tumor or lesion is identified in the brain, “it can be life-and-death for patients,” said Havelka. “Early detection and classification are critical in providing the best treatment options and outcome for patients.”

But different brain tumors and lesions can look alike, and can also resemble other neurological disorders with different treatments. As a result of this classification complexity, a patient’s treatment plan can evolve over time.

When radiologists are unable to make a conclusive diagnosis from a brain MRI scan, physicians turn to invasive brain biopsies to obtain additional information. An AI tool that can assist radiologists in making an earlier and more certain diagnosis could reduce the number of required biopsies.

Using a 3D CNN, InformAI is developing a tool that analyzes brain MRI scans to detect whether a tumor or lesion is present, and can classify an abnormal scan as one of four conditions: glioblastoma, metastatic brain tumor, multiple sclerosis or lymphoma.

The deep learning model for brain cancer detection, which is still under development, was initially trained on around 100,000 image scans from 1,000 patient studies.

Founded in 2017, InformAI is a member of the NVIDIA Inception virtual accelerator program. To learn more about the company’s work, read this recent white paper.

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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.”

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