Researchers Make Movies of the Brain with CUDA

When colleagues told Sally Epstein they sped up image processing by three orders of magnitude for a client’s brain-on-a-chip technology, she responded like any trained scientist. Go back and check your work, the biomedical engineering Ph.D. told them.

Yet it was true. The handful of researchers at Cambridge Consultants had devised a basket of techniques to process an image on GPUs in an NVIDIA DGX-1 system in 300 milliseconds, a 3,000x boost over the 18 minutes the task took on an Intel Core i9 CPU.

The achievement makes it possible for researchers to essentially watch movies of neurons firing in real time using the brain-on-a-chip technology from NETRI, a French startup.

“Animal studies revolutionized medicine. This is the next step in testing for areas like discovering new drugs,” said Epstein, head of Strategic Technology at Cambridge Consultants, which develops products and technologies for a wide variety of established companies and startups such as NETRI.

The startup designs chips that sport 3D microfluidic channels to host neural tissues and a CMOS camera sensor with polarizing filters to detect individual neurons firing. It hopes its precision imaging can speed the development of novel treatments for neurological disorders such as Alzheimer’s disease.

Facing a Computational Bottleneck

NETRI’s chips generate 100-megapixel images at up to 1,000 frames per second — the equivalent of a hundred 4K gaming systems running at 120fps. Besides spawning tons of data, they use highly complex math.

As a result, processing a single second of a recording took NETRI 12 days, an unacceptable delay. So, the startup turned to Cambridge Consultants to bust through the bottleneck.

“Our track record in scientific and biological imaging turned out to be very relevant,” said Monty Barlow, Director of Strategic Technology at Cambridge Consultants. And when NETRI heard about the 3,000x boost, “they trusted us even though we didn’t trust ourselves at first,” he quipped.

Leveraging Math, Algorithms and GPUs

A handful of specialists at Cambridge Consultants delivered the 3,000x speedup using multiple techniques. For example, math and algorithm experts employed a mix of Gaussian filters, multivariate calculus and other tools to eliminate redundant tasks and reduce peak RAM requirements.

Software developers migrated NETRI’s Python code to CuPy to take advantage of the massive parallelism of NVIDIA’s CUDA software. And hardware specialists optimized the code to fit into GPU memory, eliminating unnecessary data transfers inside the DGX-1.

The CUDA profiler helped find bottlenecks in NETRI’s code and alternatives to resolve them. “NVIDIA gave us the tools to execute this work efficiently — it happened within a week with a core team of four researchers and a few specialists,” said Epstein.

Looking ahead, Cambridge Consultants expects to find further speedups for the code using the DGX-1 that could enable real-time manipulation of neurons using a laser. Researchers also aim to explore NVIDIA IndeX software to help visualize neural activity.

The work with NETRI is one of several DGX-1 applications at the company. It also hosts a Digital Greenhouse for AI research. Last year, it used the DGX-1 to create a low-cost but highly accurate tool for monitoring tuberculosis.

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Get the Picture: For Latest in AI and Medical Imaging, Tune In to GTC Digital

Picture this: dozens of talks about AI in medical imaging, presented by experts from top radiology departments and academic medical centers around the world, all available free online.

That’s just a slice of GTC Digital, a vast library of live and on-demand webinars, training sessions and office hours from NVIDIA’s GPU Technology Conference.

Healthcare innovators across radiology, genomics, microscopy and more will share the latest AI and GPU-accelerated advancements in their fields through talks on GTC Digital.

Researchers in Sydney, Australia, are using AI to analyze brain scans. In Massachusetts, another is segmenting the prostate gland from ultrasound images to help doctors fine-tune radiation doses. And in Munich, Germany, they’re streamlining radiology reports to foster real-time reporting.

Read more about these standout speakers advancing the use of deep learning in medical imaging worldwide below. And register for GTC Digital for free to see the whole healthcare lineup.

Mental Math: Australian Center Uses AI to Analyze Brain Scans

When studying neurodegenerative disease, quantifying brain tissue loss over time helps physicians and clinical trialists monitor disease progression. Radiologists typically inspect brain scans visually and classify the brain shrinkage as “moderate” or “severe” — a qualitative assessment. With accelerated computing, brain tissue loss can instead be measured precisely and quantitatively, without losing time.

The Sydney Neuroimaging Analysis Centre conducts neuroimaging research as well as commercial image analysis for clinical research trials. SNAC will share at GTC Digital how it uses AI and NVIDIA GPUs to accelerate AI tools that automate laborious analysis tasks in their research workflow.

One model precisely isolates brain images from head scans, segmenting brain lesions for multiple sclerosis cases. The AI reduces the time to segment and determine the volume of brain lesions from up to 15 minutes for a manual examination down to just three seconds, regardless of the number or volume of lesions.

“NVIDIA GPUs and DGX systems are the core of our AI platform, and are transforming the delivery of clinical and research radiology with our AI innovation,” said Tim Wang, director of operations at SNAC. “We are particularly excited by the application of this technology to brain imaging.”

SNAC uses the NVIDIA Clara Train SDK’s AI-assisted annotation tools for model development and the NVIDIA Clara Deploy SDK for integration with clinical and research workflows. It’s also exploring the NVIDIA Clara platform as a tool for federated learning. The center relies on the NVIDIA DGX-1 server, NVIDIA DGX Station and GPU-powered PC workstations for both training and inference of its AI algorithms.

Harvard Researcher Applies AI to Prostate Cancer Therapy

Around one in nine men is diagnosed with prostate cancer at some point during his life. Medical imaging tools like ultrasound and MRI are key methods doctors use to check prostate health and plan for surgery and radiotherapy.

Davood Karimi, a research fellow at Harvard Medical School, is developing deep learning models to more quickly and accurately segment the prostate gland from ultrasound images — a difficult task because the boundaries of the prostate are often either not visible or blurry in ultrasound images.

“Accurate segmentation is necessary to make sure radiologists can deliver the needed radiation dose to the prostate, but avoid damaging critical nearby structures like the rectum or bladder,” he said.

In his GTC Digital talk, Karimi will do a deep dive into a research paper he presented at the prestigious MICCAI healthcare conference last year. Using an NVIDIA TITAN GPU, Karimi has accelerated neural network inference to under a second per scan, while improving accuracy over current segmentation techniques radiologists use.

German Company Streamlines Radiology Reports with NVIDIA Clara

Healthcare providers worldwide record their analyses of patient data, including medical images, into text-based reports. But no two radiologists or hospitals do it exactly the same.

Munich-based Smart Reporting GmbH aims to streamline and standardize the reporting workflow for radiologists. The company uses a structured reporting interface that organizes patient data and doctor’s notes into a consistent format.

Smart Reporting uses the NVIDIA Clara platform to segment prostate cancer lesions from medical images. This image annotation is loaded into a draft diagnosis report that radiologists can approve, edit or reject before generating a final report to provide to surgeons and other healthcare professionals.

A member of the NVIDIA Inception virtual accelerator program, Smart Reporting is working with major healthcare organizations including Siemens Healthineers.

“When we release a prototype for radiologists in the clinic, it’ll be essential to have almost real-time reporting,” said Dominik Noerenberg, the company’s chief medical officer. “We’re able to see that speedup running on multi-GPU containers in NGC.”

Noerenberg and Alvaro Sanchez, principal software engineer at Smart Reporting, will present a talk on the advantages of AI-enhanced radiology workflows at GTC Digital.

See the full lineup of healthcare talks on GTC Digital and register for free.

Main image shows a side-by-side comparison of brain segmentation. Left image shows manual segmentation, while right shows AI segmentation. Image courtesy of Sydney Neuroimaging Analysis Centre. 

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NVIDIA Gives COVID-19 Researchers Free Access to Parabricks

When a crisis hits, we all pitch in with what we have. In response to the current pandemic, NVIDIA is sharing tools with researchers that can accelerate their race to understand the novel coronavirus and help inform a response.

Starting today, NVIDIA will provide a free 90-day license to Parabricks to any researcher in the worldwide effort to fight the novel coronavirus. Based on the well-known Genome Analysis Toolkit, Parabricks uses GPUs to accelerate by as much as 50x the analysis of sequence data.

We recognize this pandemic is evolving, so we’ll monitor the situation and extend the offer as needed.

If you have access to NVIDIA GPUs, fill out this form to request a Parabricks license.

For researchers working with Oxford Nanopore long-read data, a repository of GPU-accelerated tools is available on GitHub. In addition, the following applications already have NVIDIA GPU acceleration built in: Medaka, Racon, Raven, Reticulatus, Unicycler.

Researchers are sequencing both the novel coronavirus and the genomes of people afflicted with COVID-19 to understand, among other things, the spread of the disease and who is most affected. But analyzing genomic sequences takes time and computing muscle.

Accelerating science has long been part of NVIDIA’s core mission. The Parabricks team joined NVIDIA in December, providing the latest tool for that work. It can reduce the time for variant calling on a whole human genome from days to less than an hour on a single server.

Given the unprecedented spread of the pandemic, getting results in hours versus days could have an extraordinary impact on understanding the virus’s evolution and the development of vaccines.

NVIDIA is inviting our family of partners to join us in matching this urgent effort to assist the research community. We’re in discussions with cloud service providers and supercomputing centers to provide compute resources and access to Parabricks on their platforms.

We’ll update this blog with links to others who can provide cloud-based access to NVIDIA GPUs and this software as those sources become available.

Support links:

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Netherlands Cancer Institute Uses Virtualization to Enhance Patient Care, Advance Cancer Research

Cancer doesn’t take nights off. To help beat the disease, the Netherlands Cancer Institute, rated one of the top 10 comprehensive cancer centers in the world, is using virtualization to have its IT infrastructure work the late shift, as well.

The NKI, which consists of both research facilities and cancer clinics, has dual goals: to accelerate research, while also improving the efficiency and productivity of its physicians.

Whether analyzing images to diagnose breast cancer or running DNA computations with large datasets, the institute relies on innovative, flexible technology to drive new discoveries in cancer research.

To keep up with the increasing needs of doctors and researchers, NKI upgraded to a state-of-the-art, software-defined infrastructure using NVIDIA virtual GPU technology powered by NVIDIA T4 GPUs and Hewlett Packard Enterprise DL380 Gen10 servers.

During the day, this virtual desktop infrastructure provides healthcare professionals with fast, flexible and secure access to patient data. At night, researchers use the same VDI platform to run computationally intensive GPU workloads.

With this high-performance yet flexible IT infrastructure, healthcare professionals can spend more time focusing on patients, while researchers can advance breakthroughs in cancer treatment.

Virtual Desktops Enhances Security and Mobility

Before NKI moved to a virtualized platform, doctors would handle patient data the old-fashioned way: working on physical desktops that stored local apps and information. The doctors would need to go to computer labs, manually log into the system, and open applications up to 20 times a day, a tedious and time-consuming process.

Maintenance and security were challenging. A few times PCs had been stolen, so NKI wanted better security to protect sensitive patient information and research data.

With VDI, physicians can move around the hospital more freely. With workstations available in each room, they can log into a virtual desktop session in one part of the hospital, then easily move to another area and get right back into their session with a swipe of their badge.

This ability to quickly switch allows NKI’s healthcare professionals to work much faster and more efficiently, providing clinicians with greater flexibility to move from one patient to another.

The VDI platform also stores all data in a safe, central environment rather than individual devices. Doctors and nurses who can securely access apps and information on mobile phones, tablets, at home or on the road.

vGPUs Bring More Power, Speed to Research 

NKI’s new infrastructure is made up of 78 HPE servers, each with three NVIDIA T4 GPUs, to provide doctors and researchers with massive GPU power for computations like DNA or image analysis.

Its clinics are busiest during the day. But most users log off in the evening, freeing up a majority of the GPU resources for cancer research. With virtualization, researchers can repurpose the T4 GPUs that aren’t in use for running complex compute workloads at night.

“Before, it would take a week for researchers to get their photos analyzed at an imaging facility,” said Roel Sijstermans, IT manager at NKI. “With the new virtual GPU infrastructure, we can send the pictures in the evening and the images will be finished by the morning.”

With data being processed overnight, researchers can analyze breast cancer tumors or increase image quality at a faster rate than before, giving doctors more time to plan their care for patients.

Register now for GTC Digital to learn more about the Netherlands Cancer Institute and how virtualization is changing the future of healthcare.

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Meet Your Match: AI Finds the Right Clinical Trial for Cancer Patients

Clinical trials need a matchmaker.

Healthcare researchers and pharmaceutical companies rely on trials to validate new, potentially life-saving therapies for cancer and other serious conditions. But fewer than 10 percent of cancer patients participate in clinical trials, and four out of five studies are delayed due to the challenges involved in recruiting participants.

For patients interested in participating in trials, there’s no easy way to determine which they’re eligible for. AI tool Ancora aims to improve the matchmaking process, using natural language processing models to pair patients with potential studies.

“This all started because my friend’s parent was diagnosed with stage 3 cancer,” said Danielle Ralic, founder and CEO of Intrepida, the Switzerland-based startup behind Ancora. “I knew there were trials out there, but when I tried to help them find options, it was so hard.”

The U.S. National Institutes of Health maintains a database of hundreds of thousands of clinical trials. Each study lists a detailed series of text-based requirements, known as inclusion and exclusion criteria, for trial participants.

While users can sort by condition and basic demographics, there may still be hundreds of studies to manually sort through — a time-consuming process of weeding through complex medical terminology.

Intrepida’s customized natural language processing models do the painstaking work of interpreting these text-heavy criteria for patients and physicians, processing new studies on NVIDIA GPUs. The studies listed in the Ancora tool are updated weekly, and users can fill out a simple, targeted questionnaire to shortlist suitable clinical trials, and receive alerts for new potential studies.

“We assessed what 20 questions we could ask that can most effectively knock a patient’s list down from, for example, 250 possible trials to 10,” Ralic said. The platform also shows patients useful information to help decide on a trial, such as how the treatment will be administered, and if it’s been approved in the past to treat other conditions.

Intrepida’s tool is currently available for breast and lung cancer patients. A physician version will soon be available to help doctors find trials for their patients. The company is a member of the NVIDIA Inception virtual accelerator program, which provides go-to-market support for AI startups — including NVIDIA Deep Learning Institute credits, marketing support and preferred pricing on hardware.

Finding the Perfect Match

Intrepida founder Danielle Ralic
Intrepida founder Danielle Ralic presented on AI and drug discovery at last year’s Annual Meeting of the Biophysical Society.

Though the primary way patients hear about clinical trials is from their physicians, less than a quarter of patients hear about trials as an option from their doctors, who have limited time and resources to keep track of existing trials.

Ralic recalls being surprised to meet a stage 4 cancer survivor while hiking in Patagonia, and finding out the man had participated in a clinical trial for a new breakthrough drug.

“I asked him, how did you know about the trial? And he said he found out through a relative of his wife’s friend. That’s not how this should work,” Ralic said.

For physicians and patients, a better and more democratized way to discover clinical trials could lead to life-saving results. It could also speed up the research cycle by improving trial enrollment rates, helping pharmaceutical companies more quickly validate new drugs and bring them to market.

As members of the NVIDIA Inception program, Ralic says she and the Intrepida team were able to meet with other AI startups and with NVIDIA developers at the GPU Technology Conference held in Munich in 2018.

“We joined the program because, as a company that was working with NVIDIA GPUs already, we wanted to develop more sophisticated natural language models,” she said. “There’s been a lot to learn from NVIDIA team members and other Inception startups.”

Using NVIDIA GPUs has enabled Intrepida to shrink training times for one epoch from 20 minutes to just 12 seconds.

Diversifying the Data

A female startup founder in an industry that to date has been dominated by men, Ralic says more diversity is key to improving the healthcare industry as a whole — and especially clinical trials.

“Healthcare is holistic. It involves so many different types of people and knowledge,” she said. “Without a diversity of perspectives, we can never address the problems the healthcare industry has.”

The data backs her up. Clinical trial participants in the United States skew overwhelmingly white and male. The lack of diversity in trials can lead to critical errors in drug dosage.

For example, in 2013, the U.S. Food and Drug Administration mandated doses for several sleeping aids to be cut in half for women. Because females metabolize the drug differently, it increased their risk of getting in a car accident the morning after taking a sleeping pill.

“If we don’t have a diverse trial population, we won’t know whether a patient of a different gender or ethnicity will react differently to a new drug,” Ralic said. “If we did it right from the start, we could improve how we prescribe medicine to people, because we’re all different.”

The post Meet Your Match: AI Finds the Right Clinical Trial for Cancer Patients appeared first on The Official NVIDIA Blog.

Aiforia Paves Path for AI-Assisted Pathology

Pathology, the study and diagnosis of disease, is a growth industry.

As the global population ages and diseases such as cancer become more prevalent, demand for keen-eyed pathologists who can analyze medical images is on the rise. In the U.K. alone, about 300,000 tests are carried out daily by pathologists.

But there’s an acute personnel shortage, globally. In the U.S., there are only 5.7 pathologists for every 100,000 people. By 2030, this number is expected to drop to 3.7. In the U.K., a survey by the Royal College of Pathology showed that only 3 percent of histopathology departments had enough staff to meet demand. In some parts of Africa, there is only one pathologist for every 1.5 million people.

While pathologists are under increasing pressure to analyze as many samples as possible, patients are having to endure lengthy wait times to get their results.

Aiforia, a member of the NVIDIA Inception startup accelerator program, has created a set of AI-based tools to speed and improve pathology workflows — and a lot more.

The company, which has offices in Helsinki and Cambridge, Mass., enables tedious tasks to be automated and for complex challenges to be solved by unveiling quantitative data from tissue samples.

This helps pathologists overcome common obstacles to the development of versatile, scalable processes for medical research, drug development and diagnostics.

“Today we can already support pathologists with AI-assisted analysis, but AI can do so much more than that,” said Kaisa Helminen, CEO of Aiforia. “With deep learning AI, we are able to extract more information from a patient tissue sample than what’s been previously possible due to limitations of the human eye.

“This way, we are able to promote new discoveries from morphological patterns and facilitate more accurate and more personalized treatment for the patient,” she said.

Hidden Figures

AI has made it possible to automate medical imaging tasks that have traditionally proved nearly impossible for the human eye to handle. And it can reveal information that was previously hidden in image data.

With Aiforia’s AI tool assisting the diagnostic process, pathologists can improve the efficiency, accuracy and reproducibility of their results.

Its cloud-based deep learning image analysis platform, Aiforia Create, allows for the rapid development of AI-powered image analysis algorithms, initially optimized for digital pathology applications.

Aiforia initially developed its platform with a focus on cancer, as well as neurological, infectious and lifestyle diseases, but is now expanding it to other medical imaging domains.

For those who want to develop algorithms for a specific task, Aiforia Create provides domain experts with unique self-service AI development tools.

Aiforia trains its image analysis AI models using convolutional neural networks on NVIDIA GPUs. These networks are able to learn, detect and quantify specific features of interest in medical images generated by microscope scanners, X-rays, MRI or CT scans.

Its users can upload a handful of medical images at a time to the platform, which uses active learning techniques to increase the efficiency of annotating images for AI training.

Users don’t need to invest in local hardware or software — instead they can access the software services via an online platform, hosted on Microsoft Azure. The platform can be deployed instantly and scales up easily.

Unpacking Parkinson’s

Aiforia’s tools are also being used to improve the diagnosis of Parkinson’s disease, a debilitating neurological condition affecting around one in 500 people.

The disease is caused by a loss of nerve cells in a part of the brain called the substantia nigra. This loss causes a reduction in dopamine, a chemical that helps regulate the movement of the body.

Researchers are working to uncover what causes the loss of nerve cells in the first place. Doing this requires collecting unbiased estimates of brain cell (neuron) numbers, but this process is extremely labor-intensive, time-consuming and prone to human error.

University of Helsinki researchers collaborated with Aiforia to mitigate the challenges of traditional methods to count neurons. Brain histology images were uploaded to Aiforia Hub, then they deployed the Aiforia Create tool to quantify the number of dopamine neurons in the samples.

Introducing the computerized counting of neurons improves the reproducibility of results, reduces the impact of human error and makes analysis more efficient.

“It’s been studied so many times that if you send the same microscope slide to five different pathologists, you get different results,” Helminen said. “Using AI can help bring consistency and reproducibility, where AI is acting as a tireless assistant or like a ‘second opinion’ for the expert.”

The study carried out at the University of Helsinki would typically have taken weeks or even months without AI. Using Aiforia’s tools, the research team was able to achieve a 99 percent speed increase, freeing up their time to further their work into finding a cure for Parkinson’s.

Aiforia Create is sold with a research use status and is not intended for diagnostic procedures.

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From ER Visit to AI Startup, CloudMedx Pursues Predictive Healthcare Models

Twice Sahar Arshad’s father-in-law went to an emergency room in Pakistan complaining of frequent headaches. Twice doctors sent him home with a diagnosis of allergies.

Turns out he was suffering from a subdural hematoma — bleeding inside the head. Following the second misdiagnosis, he went into a coma and required emergency brain surgery (and made a full recovery.)

Arshad and her husband, Tashfeen Suleman — both computer scientists living in Bellevue, Wash., at the time — afterwards tried to get to the root of the inaccurate diagnoses. The hematoma turned out to be a side effect of a new medication Suleman’s father had been prescribed a couple weeks prior. And he lacked physical symptoms like slurred speech and difficulty walking, which would have prompted doctors to order a CT scan and detect the bleeding earlier.

Too Much Data, Too Little Time

It’s a common problem, Arshad and Suleman found. Physicians often have to rely on limited information, either because there’s insufficient data on a patient or because there’s not enough time to analyze large datasets.

The couple thought AI could help address this challenge. In late 2014, they together founded CloudMedx, a Palo Alto-based startup that develops predictive healthcare models for health providers, insurers and patients.

A member of the NVIDIA Inception virtual accelerator program, CloudMedx is working with the University of California, San Francisco; Barrow Neurological Institute, a member of Dignity Health, a nonprofit healthcare organization; and some of the largest health insurers in the country.

Its AI models, trained using NVIDIA V100 Tensor Core GPUs through Amazon Web Services, can help automate medical coding, predict disease progression and determine the likelihood a patient may have a complication and need to be readmitted to the hospital within 30 days.

“What we’ve built is a natural language model that understands how different diseases, symptoms and medications are related to each other,” said Arshad, chief operating officer at CloudMedx. “If we’d had this tool in Tashfeen’s father’s case, it would have flagged the risk of internal head hemorrhaging and recommended obtaining a CT scan.”

Working with an AI to Risk Assessment

The CloudMedx team has developed a deep neural network that can process medical data to provide risk assessment scores, saving clinicians time and providing personalized insight for patients. It’s trained on a dataset of 54 million patient encounters.

In a study to evaluate its deep learning model, the clinical AI tool took a mock medical exam — and outperformed human doctors by 10 percent, on average. On their own, physicians scored between 68 to 81 percent. When taking the exam along with CloudMedx AI, they achieved a high score of 91 percent.

The startup’s AI models are used in multiple tools, including a coding analyzer that converts doctor’s notes into a series of medical codes that inform the billing process, as well as a clinical analyzer that evaluates a patient’s health records to generate risk assessments.

CloudMedx is collaborating with UCSF’s Division of Gastroenterology to stratify patients awaiting liver transplants based on risk, so that patients can be matched with donors before the tumor progresses too far for a transplant.

The company is also working with one of the largest health insurers in the U.S. to better identify congestive heart failure patients with a high risk of readmission to the hospital. With these insights, health providers can follow up more often with at-risk patients, reducing readmissions and potentially saving billions of dollars in treatment costs.

Predictive Analytics for Every Healthcare Player

Predictive analytics can even improve the operational side of healthcare, giving hospitals a heads-up when they might need additional beds or staff members to meet rising patient demand.

“It’s an expensive manual process to find additional resources and bring on extra nurses at the last minute,” Arshad said. “If hospitals are able to use AI tools for surge prediction, they can better plan resources ahead of time.”

In addition to providing new insights for health providers and payers, these tools save time by processing large amounts of medical data in a fraction of the time it would take humans.

CloudMedx has also developed an AI tool for patients. Available on the Medicare website to its 53 million patient beneficiaries, the system helps users access their own claims data, correlates a person’s medical history with symptoms, and will soon also estimate treatment costs.

NVIDIA Inception Program

As members of the NVIDIA Inception program, the CloudMedx team was able to reach out to NVIDIA developers and the company’s healthcare team for help with some of the challenges they faced when scaling up for cloud deployment.

Inception helps startups during critical stages of product development, prototyping and deployment with tools and expertise to help early-stage companies grow.

Both Suleman and Arshad have spoken at NVIDIA’s annual GPU Technology Conference, with Arshad participating in a Women@GTC healthcare panel last year. The conference has helped the team meet some of their customers, said Arshad, who’s also a finalist for Entrepreneur of the Year at the 2020 Women in IT Awards New York.

Check out the healthcare track for GTC, taking place in San Jose, March 22-26.

The post From ER Visit to AI Startup, CloudMedx Pursues Predictive Healthcare Models appeared first on The Official NVIDIA Blog.

Putting AI on Trials: Deep 6 Speeds Search for Clinical-Study Recruits

Bringing a new medical treatment to market is a slow, laborious process — and for a good reason: patient safety is the top priority.

But when recruiting patients to test promising treatments in clinical trials, the faster the better.

“Many people in medicine have ideas of how to improve healthcare,” said Wout Brusselaers, CEO of Pasadena, Calif.-based startup Deep 6 AI. “What’s stopping them is being able to demonstrate that their new process or new drug works, and is safe and effective on real patients. For that, they need the clinical trial process.”

Over the past decade, the number of cancer clinical trials has grown 17 percent a year, on average. But nearly a fifth of these studies fail to recruit a sufficient number of participants that fit sometimes very specific trial criteria after three years of searching — and the problem isn’t getting any simpler.

“In the age of precision medicine, clinical trial criteria are getting more challenging,” Brusselaers said. “When developing a drug that is targeting patients with a rare genetic mutation, you have to be able to find those specific patients.”

By analyzing medical records with AI, Deep 6 can identify a patient population for clinical trials within minutes, accelerating what’s traditionally a months-long process. Major cancer centers and pharmaceutical companies, including Cedars Sinai Medical Center and Texas Medical Center, are using the AI tool. They’ve matched more than 100,000 patients to clinical trials so far.

The startup’s clinical trial acceleration software has specific tools to help hospitals recommend available trials to patients and to help pharmaceutical companies track and accelerate patient recruitment for their studies. Future versions of the software could also be made available for patients to browse trials.

A Match Made in AI

Deep 6 AI is a member of the NVIDIA Inception virtual accelerator program, which helps startups scale faster. The company uses an NVIDIA TITAN GPU to accelerate the development of its custom AI models that analyze patient data to identify and label clinical criteria relevant to trials.

“It’s more efficient and less expensive for us to develop our models on premises,” Brusselaers said. “We could turn around models right away and iterate faster, without having to wait to rerun the code.”

While the tool can be used for any diagnostic area or medical condition, Brusselaers says over a quarter of trials on the platform are oncology studies, followed closely by cardiology.

Trained on a combination of open-source databases and real-world data from Deep 6’s partners, the AI models first identify specific mentions of clinical terminology and medical codes in patient records with natural language processing.

Additional neural networks analyze unstructured data like doctor’s notes and pathology reports to gather additional information about a patient’s symptoms, diagnoses and treatments — even detecting potential conditions not mentioned in the medical records.

Deep 6’s tool then creates a patient graph that represents the individual’s clinical profile. These graphs can easily be matched by doctors and researchers to develop trial cohorts, upgrading a time-consuming, often unfruitful manual process.

Researchers at Los Angeles’ Cedars-Sinai Smidt Heart Institute — one of the startup’s clients — had enrolled just two participants for a new clinical trial after six months of recruitment effort. Using Deep 6 AI software, they found 16 qualified candidates in an hour.

Texas Medical Center, a collection of over 60 health institutions, is rolling out Deep 6 software across its network to replace the typical process of finding clinical trial candidates, which requires associates to manually flip through thick folders of medical records.

“It’s just a long slog to find patients for clinical trials,” said Bill McKeon, CEO of Texas Medical Center. Using Deep 6’s software tool “is just completely transforming.”

McKeon says in one case, it took six months to find a dozen eligible patients for a trial with traditional recruitment efforts. The same matching process through Deep 6’s software found 80 potential participants in minutes.

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From Point AI to Point TB: DeepTek Detects Tuberculosis from X-Rays

Tuberculosis is an issue close to home for Pune, India-based healthcare startup DeepTek. India has the world’s highest prevalence of the disease — accounting for over one-quarter of the 10 million new cases each year.

It’s a fitting first project for the company, whose founders hope to greatly improve global access to medical imaging diagnostics with an AI-powered radiology platform. DeepTek’s DxTB tool screens X-ray images for pulmonary TB, flagging cases for prioritized review by medical experts.

India aims to eradicate TB by 2025, five years before the United Nations’ global goal to end the epidemic by 2030. Chest X-ray imaging is the most sensitive screening tool for pulmonary TB, helping clinicians determine which patients should be referred for further lab testing. But two-thirds of people worldwide lack access to even basic radiology services, in part due to high costs and insufficient infrastructure.

“There’s a huge shortage of imaging experts available to read X-ray scans,” said Amit Kharat, CEO of the startup and a clinical radiologist. “Since radiologists’ time is often sought for more demanding investigations like CT or MRI scans, this is an important gap where AI can add value.”

DeepTek is a member of NVIDIA Inception, a virtual accelerator program that enables early-stage companies with fundamental tools, expertise and go-to-market support. The startup uses NVIDIA GPUs through Google Cloud and Amazon Web Services for training and inference of its deep learning algorithms.

Its DxTB tool has been used to analyze over 70,000 chest X-rays so far in partnership with the Greater Chennai Corporation’s TB Free Chennai Initiative, a project supported by the Clinton Health Access Initiative. The system is deployed in mobile vans equipped with digital X-ray machines to conduct TB screening for high-risk population groups.

mobile TB clinic in Chennai, India

As patients are screened in the mobile clinics, scans of the chest X-ray images are securely transmitted to the cloud for inference. The accelerated turnaround time allows doctors to triage cases and conduct additional tests right away, minimizing the number of patients who don’t follow up for further testing and treatment.

“Doing this job would have taken a month’s time. With AI, it’s now feasible to do it within hours,” Kharat said. “The goal is to make sure not a single patient is lost.”

 DxTB can be deployed in the cloud or — where internet connections are weak or unavailable — as an edge service. Radiologists access the scans through a dashboard that enables them to review the studies and provide expert feedback and validation.

Patients detected as potentially TB-positive provide a sputum, or lung fluid, sample that undergoes a molecular test before doctors confirm the diagnosis and prescribe a medication program.

In addition to the mobile clinics, around 50 imaging centers and hospitals in India use DeepTek’s AI models. One hospital network will soon deploy the startup’s ICU Chest tool, which can diagnose a score of conditions relevant to intensive care patients.

DeepTek is also developing models to screen X-rays of the joints and spine; CT scans of the liver and brain; and brain MRI studies. The company’s in-house radiologists annotate scans by hand for training, validation and testing.

To further improve its deep learning network, the startup uses data augmentation and data normalization — and incorporates users’ radiology reports as feedback to refine and retrain the AI.

DeepTek now processes nearly 25,000 imaging studies a month on its cloud platform.

“Building AI models is just one part of the story,” Kharat said. “The whole technology pipeline needs to integrate smoothly with the radiology workflow at hospitals, imaging centers and mobile clinics.”

Main image shows a calcified nodule in the lung’s right upper lobe, detected by DeepTek’s AI model.

The post From Point AI to Point TB: DeepTek Detects Tuberculosis from X-Rays appeared first on The Official NVIDIA Blog.

AI, Accelerated Computing Drive Shift to Personalized Healthcare

Genomics is finally poised to go mainstream, with help from deep learning and accelerated-computing technologies from NVIDIA.

Since the first human genome was sequenced in 2003, the cost of whole genome sequencing has steadily shrunk, far faster than suggested by Moore’s law. From sequencing the genomes of newborn babies to conducting national population genomics programs, the field is gaining momentum and getting more personal by the day.

Advances in sequencing technology have led to an explosion of genomic data. The total amount of sequence data is doubling every seven months. This breakneck pace could see genomics in 2025 surpass by 10x the amount of data generated by other big data sources such as astronomy, Twitter and YouTube — hitting the double-digit exabyte range.

New sequencing systems, like the DNBSEQ-T7 from BGI Group, the world’s largest genomics research group, are pushing the technology into broad use. The system generates a whopping 60 genomes per day, equaling 6 terabytes of data.

With advancements in BGI’s flow cell technology and acceleration by a pair of NVIDIA V100 Tensor Core GPUs, DNBSEQ-T7 sequencing is sped up 50x, making it the highest throughput genome sequencer to date.

As costs decline and sequencing times accelerate, more use cases emerge, such as the ability to sequence a newborn in intensive care where every minute counts.

Getting Past the Genome Analysis Bottleneck: GPU-Accelerated GATK

NVIDIA Parabricks GPU-accelerated GATK

The genomics community continues to extract new insights from DNA. Recent breakthroughs include single-cell sequencing to understand mutations at a cellular level, and liquid biopsies that detect and monitor cancer using blood for circulating DNA.

But genomic analysis has traditionally been a computational bottleneck in the sequencing pipeline — one that can be surmounted using GPU acceleration.

To deliver a roadmap of continuing GPU acceleration for key genomic analysis pipelines, the team at Parabricks — an Ann Arbor, Michigan-based developer of GPU software for genomics — is joining NVIDIA’s healthcare team, NVIDIA founder and CEO Jensen Huang shared today onstage at GTC China.

Teaming up with BGI, the Parabricks’ software can analyze a genome in under an hour. Using a server with eight NVIDIA T4 Tensor Core GPUs, BGI showed the throughput could lower the cost of genome sequencing to $2 — less than half the cost of existing systems.

See More, Do More with Smart Medical Devices

New medical devices are being invented across the healthcare industry. United Imaging Healthcare has introduced two industry-first medical devices. The uEXPLORER is the world’s first total body PET-CT scanner. Its pioneering ability to image an individual in one position enables it to carry out fast, continuous tracking of tracer distribution over the entire body.

A full body PET/CT image from uEXPLORER. Courtesy of United Imaging.

The total-body coverage of uEXPLORER can significantly shorten scan time. Scans as brief as 30 seconds provide good image quality, compared to traditional systems requiring over 20 minutes of scan time. uEXPLORER is also setting a new benchmark in tracer dose — imaging at about 1/50 of the regular dose, without compromising image quality.

The FDA-approved system uses 16 NVIDIA V100 Tensor Core GPUs and eight 56 GB/s InfiniBand network links from Mellanox to process movie-like scans that can acquire up to a terabyte of data. The system is already deployed in the U.S. at the University of California, Davis, where scientists helped design the system. It’s also the subject of an article in Nature, as well as videos watched by nearly half a million viewers on YouTube.

United’s other groundbreaking system, the uRT-Linac, is the first instrument to support a full radiation therapy suite, from detection to prevention.

With this instrument, a patient from a remote village can make the long trek to the nearest clinic just once to get diagnostic tests and treatment. The uRT-Linac combines CT imaging, AI processing to assist in treatment planning, and simulation with the radiation therapy delivery system. Using multi-modal technologies and AI, United has changed the nature of delivering cancer treatment.

Further afield, a growing number of smart medical devices are using AI for enhanced signal and image processing, workflow optimizations and data analysis.

And on the horizon are patient monitors that can sense when a patient is in danger and smart endoscopes that can guide surgeons during surgery. It’s no exaggeration to state that, in the future, every sensor in the hospital will have AI-infused capabilities.

Our recently announced NVIDIA Clara AGX developer kit helps address this trend. Clara AGX comprises hardware based on NVIDIA Xavier SoCs and Volta Tensor Core GPUs, along with a Clara AGX software development kit, to enable the proliferation of smart medical devices that make healthcare both smarter and more personal.

Apply for early access to Clara AGX.

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