| April 2, 2019

Nvidia introduced Clara AI, the new star toolkit built for radiologists

Anca Spanu

Anca's career in journalism spans over 2 decades. She has served as staff writer, editor and deputy chief editor at... Anca's career in journalism spans over 2 decades. She has served as staff writer, editor and deputy chief editor at various media outlets all over the world. At Healthcare Weekly, Anca writes about current events, innovations in the healthcare space and events/ conferences with a focus on investing & startups.

When looking into the future of healthcare, two major components are of significance: qualified personnel and financing. Keeping in mind that qualified humans need equally good technology, and that ensuring the best patient outcome depends on having money, it is fair to say that AI and machine-learning have become the must-haves of the healthcare system. Specialists are continuing to find new innovative ways to use them.

AI is ready to unlock massive potential in hospital systems, especially in one of the areas where deep learning holds the most promise: medical imaging.

That’s why NVIDIA introduced Clara AI, a toolkit that includes 13 state-of-the-art classification and segmentation AIs, and software tools built for radiologists.

Leading medical institutions around the world are already using the Clara platform to put the power of AI into the hands of radiologists and take advantage of the growing ecosystem of researchers and startups.

What is healthcare AI and how is it helping?

Artificial intelligence in the medical field relies on the analysis and interpretation of huge quantities of data sets.

It basically can help in any aspect of the medical chain. It helps doctors: make diagnoses more easily, make better decisions, effectively manage patient data information, and create personalized medicine plans.

 

AI is an emerging technology that is finding its way into many facets of the pharma sector, from drug development to diagnosis and even patient care.

AI helps alleviate the burden of healthcare related bureaucracy, by organizing billing, patient records and internal document circuits.

Each patient has unique healthcare needs and preferences, making personalized care more critical than ever before; it can be accomplished by using AI.

How NVIDIA’s Clara Lowers Barriers of AI

Labeled data is critical to building safe and robust AI, but radiologists’ time is too precious to spend hours labeling datasets. The Clara AI assisted annotation capability helps reduce the time needed to make the necessary annotations from hours to mere minutes.

In fact, MITK (Medical Imaging Interaction Toolkit) developers from the German Cancer Research Center (DKFZ) already integrated Clara AI into their open source viewer, used by thousands worldwide.

Clara AI is also capable of transferring learning, which enables it to adapt existing models to fit local variables. Deep learning algorithms are customized to work with specific types of data such as local demographics and imaging devices, rendering the moving or sharing of patient data unnecessary. This enables doctors to build models for their own patients with 10x less data than if they had started from scratch.

Integrating AI models and applications into existing hospital IT systems takes a huge amount of technical expertise. Clara AI facilitates the integration of AI models into preexisting radiology workflows using industry standards, like DICOM.

This possibility translates into exciting news: hospitals, research institutions, and the whole medical imaging industry can integrate Clara AI. The toolkit includes two software development toolkits: the Clara Train SDK and Clara Deploy SDK, which can be accessed from NGC, and deployed on a hospital-ready infrastructure: NVIDIA T4 server and NVIDIA DGX POD.

Neural networks have been used for years to interpret visual data. Deep learning models bring a better ability to identify certain features in images, to enhance image quality, and spot abnormalities.

This is how AI is able to transform radiology, saving time and improving patient care, all while saving money for the healthcare organizations.

Important medical institutions already use NVIDIA Clara AI

Potential Clara AI clients don’t need to imagine how the system works, it can be tested live, because major medical institutions around the world are already using the Clara platform. These institutions chose to empower their radiologists and take advantage of the growing ecosystem of researchers and startups.

Ohio State University

The Ohio State University Wexner Medical Center is the first U.S. partner to use the NVIDIA Clara platform for AI-powered clinical imaging. OSU, a renowned academic medical center, will deploy deep learning models useful for various applications, including early warning systems in an ER department and diagnostic assistants.

Using Clara AI, the Ohio State University radiologists proved that the learning transfer works. They were able to incorporate a model developed at another institution, by providing for it a local annotated dataset which helped adapt the model to OSU patients.

National Institutes of Health

The National Institutes of Health Clinical Center, which is the largest research hospital in the USA, was the place where NVIDIA scientists used Clara AI to develop a method to separate the prostate from the surrounding tissue on an MRI. The performance achieved by the localized model was similar to that of a radiologist. It also surpassed other state-of-the-art algorithms that were trained and evaluated on data from the same domain.

University of California, San Francisco

Another famous medical institution, UCSF, which hosts a top-ranked radiology residency program, is using a Clara AI-powered scalable infrastructure to create a pathway for future doctors, making it easier for them to adopt the system, by creating, putting to proof and deploying multiple AI algorithms across radiology.

“We have an incredibly innovative group of researchers who are building clinically valuable AI tools, and need a consistent way to validate and deploy these tools into clinical workflows,” said Christopher Hess, chair of radiology, UCSF. “NVIDIA Clara will be an essential component of the medical imaging AI ecosystem that enables us to develop and deploy our own and external AI models.


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