A bodybuilder, a cyclist and a student.

They didn’t walk into a bar. But they did raise some hair-raising fraudulent insurance claims.

In 2017, a cyclist claimed £135,000 compensation after he falsely stated that he fell off his bike following a collision with a pothole. A bodybuilder claimed £150,000 for a back injury that wasn’t hindering him from the press-up challenge he went on to film. And a student thought his luck was in when he tried to claim £14,000 for the “loss” of some of his more expensive personal items while on a jolly holiday in Venice.

Insurance fraud cases cost the U.K. billions of pounds every year. On average, it boils down to over £10,000 per fraudulent claim — and results in consumers having to spend an extra £50 per policy.

To drive these numbers down, Intelligent Voice, Strenuus and the University of East London are creating an AI and voice recognition technology that will help identify fraudulent claims.

Tackling the Big Issues

Insurance companies currently face two major challenges.

The first is the large number of calls they receive for fraudulent claims. The second is adapting to the recent GDPR law, which prohibits so-called black box policies. Instead, insurance companies have to be able to explain to their customers, as well as regulators, how decisions have been made.

In response, London-based Intelligent Voice has set out to develop a set of machine learning algorithms that can identify fraudulent behavior in real time. The goal is to make processes more efficient and effective, as well as reduce the fatigue experienced by call agents.

Intelligent Voice combines its machine learning and speech recognition skills with behavioral analytics knowledge from Strenuus, also based in London. The University of East London is working on adding an explainability layer to the technology that will determine how and when decisions were made in a particular case.

The team has shown that they can match human-level efforts in identifying potential fraud.

Their efforts are part of the U.K. government’s Next Generation Services Industrial Strategy Challenge Fund. The project will run for about two-and-a-half years.

Detecting Fraud Before the Payout

During calls to insurers, the system picks up signals of potential deception. These can take the form of specific words or phrases as well as tone of voice. A long short-term memory (LSTM) network has been trained to recognize the signals in real time, so call agents can respond to alerts immediately and change their responses accordingly.

Employee productivity gets a boost because calls flagged by the technology can be provided as a list noting potentially fraudulent markers. Call agents can jump directly to flagged sections for review.

Intelligent Voice’s machine learning algorithms are trained using hundreds of thousands of insurance calls, which have already been manually screened. To power this training, they use an assortment of NVIDIA GPUs and, in production, their software runs on NVIDIA Tensor Core V100 GPUs.

“From a technology perspective, we’ve not found anything which gives us the flexibility and performance that NVIDIA GPUs do,” said Nigel Cannings, CTO of Intelligent Voice. “The flexibility that CUDA offers, in particular, both on the programming side as well as supporting deep learning simultaneously, means that NVIDIA is the obvious choice for us.”

The post UK Government Aims to Tackle Insurance Fraud with AI appeared first on The Official NVIDIA Blog.

Andras Wirth is like many early AI researchers: His deep learning ambitions only turned into reality because of a sea change in technology.

A physicist, Wirth wanted to run Monte Carlo algorithms to make leaping advances in nuclear imaging, which was previously computationally impossible without massive supercomputers.

A decade ago, his breakthrough came when his lab began using GPUs and the first CUDA release on the computationally demanding algorithms.

On Thursday at the GPU Technology Conference in Silicon Valley, Wirth, who leads nuclear imaging at Mediso Medical, spoke about his company’s groundbreaking work.

Wirth’s team of CUDA programmers runs Monte Carlo method transport calculations on GPUs to enhance image quality. This helps to eliminate the usual degenerating effects that come from inaccuracies in physical modeling.

Monte Carlo transport methods rely on modeling the physical processes that contribute to acquiring the image of a patient. For maximum precision, the modeling consists of simulating billions of photon tracks. These photon tracks are random by nature, thus the simulation itself has to be random —  just like the games in the city of Monte Carlo.

Besides improving the image quality of scans, the main issue for nuclear medicine is the need to lower the dose of injected radioactive isotopes without impairing the diagnostic value of the acquired images. Neural networks help cope with the increasing noise level while also maintaining the useful information with a performance that is unrivaled by  conventional methods.

The lowered dosages are a boon to patients and the facilities that administer the radioactive substances, and the GPU-accelerated technique behind it holds great promise across the field.

“This is a complete game changer — it can have an effect on every type of nuclear medical procedure,” Wirth said.

Los Alamos to Budapest

The Monte Carlo method dates back to research at the Manhattan Project in the 1940s. But it wasn’t until recently that researchers and engineers applied GPUs to the computationally demanding algorithms.

Wirth’s work with GPUs on Monte Carlo methods have added to the capabilities of Budapest-based Mediso’s software used in its cameras for SPECT scans. SPECT (single-photon emission computerized tomography) scans rely on radioisotopes that are injected into the bloodstream of patients. Clinicians then use specialized cameras to capture 3D images of organs.

Mediso trained its U-Net convolutional neural network architecture on 1,000 images of bone scans. U-nets are used in medical imaging to bolster image segmentation so that different areas of details can be outlined.

It took a lot of computing power to do these types of calculations, Wirth said. “Traditionally, only supercomputers were able to do these type of calculations,” he said. “Until, GPUs appeared for general computing, it didn’t even make sense to try out Monte Carlo particle transport calculations in medical imaging.”

GPUs Lower Dose

Radioisotopes administered in medical imaging are low-level carcinogens for patients, expensive for imaging facilities to obtain and require special handling.

“Nobody likes to have nuclear isotopes in their body. That’s why we want to minimize the dose injected to the body — there are risks,” said Wirth.

However, when you lower a radioisotope dose, those lines are more difficult to decipher and blurring occurs that makes it difficult to spot lesions in bones.

Mediso used its neural network solutions running on GPUs to help to minimize that imaging “noise” while reducing the radioisotope dose administered to patients by one-eighth.

“It’s hard to imagine developing neural network-based products without the help of GPUs nowadays. It doesn’t stop there, however: since processing time is crucial in medical imaging, GPU technology has become a vital element of imaging products,” Wirth said.

The post Betting on Monte Carlo: GPUs a ‘Game Changer’ for Nuking Noise in Nuclear Imaging appeared first on The Official NVIDIA Blog.

A credit card swipe gets you into the checkout-free miniature convenience store. After that, just grab Oreos, Pringles or other munchies, check your receipt and go.

Startup AiFi presented its automated retail store, dubbed the NanoStore, at the GPU Technology Conference this week.

The Silicon Valley-based company uses image recognition powered by a single NVIDIA T4 GPU to automatically capture customers’ shopping items and charge them.

AiFi — an NVIDIA Inception winner last year — is now in pilot tests with its NanoStores and offers its store technology to retailers of all sizes.

The NVIDIA Inception program is a virtual accelerator that helps startups get to market faster.

AiFi’s NanoStores are built into a shipping container that can hold more than 500 different products. The NanoStore concept fills a niche in the market between a vending machine and a convenience store, said co-founder and CEO Steve Gu.

“There’s a gap between vending machines and convenience stores. We believe this will be the next big thing,” said Gu.

Snack Tracking

NanoStores pack cameras inside to capture a customer’s merchandise choices, which are identified by AiFi’s image recognition algorithms and then put on the tab.

It’s not easy to recognize the merchandise and connect it with the customer, and the startup continues to work on this, Gu said.

Detecting more than 500 different products was made easier by using 3D simulations. That made it possible to create about thousands of images from different angles for each product to refine their training set.

Training time was accelerated by using workstations sporting NVIDIA TITAN series GPUs, Gu said.

NanoStore Pilots

AiFi’s NanoStore offers retailers an easy way to try out a fully automated store that is always open, extending hours and sales, Gu told attendees of his GTC talk.

“It creates a new line of business for convenience stores.”

The company is working with Valora, based in Switzerland, on a pilot of its NanoStores located at European railway stations. The startup is also working on a pilot with Carrefour, a French retail giant with more than 12,000 stores, for its technology.

Closer to home, AiFi is in discussions with some universities to place pilots of its NanoStores, which could operate 24/7 on their campuses.

“Students never sleep and neither does the NanoStore,” Gu said.

The post Snack Shacks: Startup Shows Off Self-Service Stores appeared first on The Official NVIDIA Blog.

Nearly a third of physicians will be sued at least once in their careers — most commonly for an error in diagnosis. Medical errors are also the third-leading cause of death in the United States, according to a study by Johns Hopkins Medicine.

Deep learning has the potential to help doctors cut down on diagnostic errors, said cardiologist Rima Arnaout in a talk at the GPU Technology Conference.

An assistant professor of medicine at the University of California, San Francisco, Arnaout is focusing on the potential of AI to analyze cardiac ultrasounds and detect congenital heart disease from fetal ultrasounds.

“In medicine, a picture is worth more than a thousand words,” she said. “It really can be worth a patient’s life, in some cases.”

What Can AI Do to Help?

Arnaout outlined a few key challenges for humans analyzing medical images. For one, people sometimes make mistakes. There’s also a physical limit to how much data cardiac imaging specialists like cardiologists and radiologists can analyze.

“We cannot allow those kinds of shortcomings,” she said. “We need accuracy, precision, and we need it delivered at scale.”

While AI models are not without their limitations, Arnaout said, they can help clinicians use medical imaging techniques like ultrasound to their full potential.

She turned to echocardiogram data because “it’s balanced in terms of information richness and clinical volume compared to other cardiovascular imaging tools.” Since echocardiograms can be used for the diagnosis and management of almost every cardiovascular disease, she said, there’s very little selection bias in the datasets.

Echocardiograms are a challenging training dataset, however, because one ultrasound study consists of still images and videos captured from over a dozen angles. A study Arnaout’s team published in npj Digital Medicine used deep learning to classify 15 of these standard views, achieving 91.7 percent accuracy on low-resolution images.

Detecting Congenital Heart Disease in Utero

Recent work by Arnaout focused on the detection of congenital heart disease from fetal ultrasounds. While in theory more than 90 percent of complex congenital heart disease cases can be diagnosed through traditional fetal screening ultrasounds, the actual detection rate is under 50 percent.

This gap occurs in part because fetal hearts are small and fast beating. Since the fetus itself is often moving, diagnostic-quality images can be difficult to obtain. And although it’s the most common birth defect, congenital heart disease affects just one percent of live births. Since it’s so rare, the condition can easily go overlooked by human readers.

That’s where an algorithm can help: once trained, it could reliably catch congenital heart disease in perpetuity.

Using hundreds of fetal echocardiograms from 18 to 24 weeks of gestational age, the UCSF researchers developed convolutional neural networks to distinguish two varieties of congenital heart disease. The deep learning model, trained on NVIDIA GPUs hosted on Amazon Web Services, was able to classify the two kinds of defects at well above the average diagnostic rate.

Catching heart defects early can lead to better outcomes for patients after birth. And if certain types of lesions are spotted in a fetal ultrasound, doctors can recommend in-utero therapies that significantly improve the heart’s condition by birth.

Arnaout said, “This has the potential to really affect the natural history of an entire life.”

Main image is of an echocardiogram showing a ventricular septal defect, or hole in the heart — a common congenital heart defect. Photo by Kjetil Lenes/Ekko, licensed under public domain on Wikimedia Commons.

The post Error Parer: How AI Could Help Cardiologists Detect Heart Defects Without Skipping a Beat appeared first on The Official NVIDIA Blog.