Artificial intelligence research in radiology has shown great promise to improve quality, access and cost of care. However, it's going to take an army of allies to bring this research to clinical practice. And that's why NVIDIA has been hard at work expanding our ecosystem of healthcare partners.
We're now working with 75 partners to apply AI to healthcare. It's a number that grows every month. And it includes a diverse collection of medical centers, medical imaging companies, research institutes, healthcare startups and healthcare providers.
Many of these partners will be joining us at the Radiological Society of North America convention, taking place this week in Chicago. In addition to showcasing some of our work with them at RSNA, we're announcing several important developments:
Availability of the NVIDIA Clara software development kit (SDK)
Unveiling of the Transfer Learning Toolkit for Medical Imaging and AI Assisted Annotation SDK
Ohio State University is partnering with NVIDIA to build the first in-house AI marketplace using the NVIDIA Clara platform
National Institutes of Health is partnering with NVIDIA to bring AI tools to clinical trials
Intelligent Imaging: Clara SDK Available Now With the newly available Clara SDK, developers can easily take advantage of any GPU platform they have to deploy AI, visualization or compute-intensive applications such as image reconstruction.
NVIDIA GPUs have played a key role in medical imaging for more than a dozen years. Diagnostic imaging modalities rely on our GPUs to deliver real-time, state-of-the-art image reconstruction. This includes iterative reconstruction to reduce radiation dosage of CT scans, compressed sensing to decrease scan time in MRIs, and software beamforming to increase image quality from ultrasounds.
Yet AI is capable of making image acquisition even better. Imaging instruments need AI to ensure the highest quality images are being acquired. Imaging companies like United Imaging, Fujifilm and Canon have all deployed NVIDIA DGX supercomputers as the AI infrastructure to accelerate their development of AI.
The Clara SDK is part of the open NVIDIA Clara platform, which enables the medical imaging industry to create and deploy advanced imaging applications and AI-enabled workflows.
MGH & BWH Center for Clinical Data Science has adopted the NVIDIA Clara SDK as a part of their AI deployment strategy. They have developed an Abdominal Aortic Aneurysm detection model and are deploying it on the Nuance AI Marketplace powered by NVIDIA Clara.
If radiology is to benefit from the thousands of new AI applications being developed, we will need to have a clear path to deployment at a broad spectrum of clinical and imaging centers. This deployment path is key to a scalable adoption of AI in radiology, said Mark Michalski, executive director at MGH & BWH Center for Clinical Data Science.
Learn more about the Clara SDK collection of GPU-accelerated software tools, libraries, AI engines, containers and sample applications.
Radiology Workflows Need Thousands of Algorithms Changing the practice of radiology is going to require thousands of applications. The demand for AI applications and the need to adapt them for each institution's patients, machines and practice is why over 50 leading healthcare institutions - including MGH, BWH, NIH, UCSF, OSU, Mayo and KCL - have invested in NVIDIA DGX systems to develop AI applications.
To boost the radiology industry's ability to build and adapt AI applications, NVIDIA has announced two key technologies:
The AI Assisted Annotation SDK enables radiologists to unlock the value of their data 10x faster than traditional annotation methods.
The Transfer Learning Toolkit for Medical Imaging enables physicians to customize and adapt AI applications to their own patients. This is critical because every radiology practice is unique, with its own instruments, protocols and patient demographics.
At OSU we understand the importance of these tools. Data curation is one of the major bottlenecks in the algorithm development lifecycle. This is particularly true in medical imaging due to the inherent complexity of the data and limited availability of highly trained annotators, said Luciano Prevedello, chief of the Imaging Informatics Division at the Ohio State University Wexner Medical Center.
Techniques such as transfer learning employed by the toolkit can significantly decrease the number of images required for training without detriment to algorithm performance, Prevedello continued. This, in combination with a more efficient data curation process that leverages AI to prepare the cases, will open the doors to a new era in algorithm development.
Ohio State University Builds First In-House AI Marketplace The Ohio State University Wexner Medical Center, a leading-edge academic medical center and university, is the first U.S. partner to adopt the NVIDIA Clara platform to build an in-house AI marketplace for clinical imaging.
OSU's AI marketplace will allow radiologists to quickly apply deep learning and machine learning to their workflows.
The rapid adoption of artificial intelligence has opened new opportunities in medical imaging, said Dr. Richard White, chair of the department of radiology, Medical Imaging Informatics at the Ohio State University Wexner Medical Center. Working with NVIDIA, we've streamlined the process of integrating AI into the workflow, which will lead to improved patient outcomes.
OSU will deploy deep learning and machine learning to improve clinical responsiveness in urgent conditions, like detection of a brain hemorrhage or coronary artery disease. These algorithms could be integrated into many clinical workflows, such as an early warning system in an ER department, a worklist optimization in a radiology lab or as a diagnostic a
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Artificial intelligence research in radiology has shown great promise to improve quality, access and cost of care. However, it's going to take an army of allies to bring this research to clinical practice. And that's why NVIDIA has been hard at work expanding our ecosystem of healthcare partners.
