November 28th - NVIDIA launches new software at the North American Society of Radiology Annual Meeting (RSNA) and announces new partners to optimize care quality, channels and costs.
Artificial intelligence research in the field of radiology has shown great potential in improving the quality, channels and costs of care. However, if the study is to be applied to clinical practice, we still need the support of our partners. Because of this, NVIDIA has spared no effort to expand its healthcare partner ecosystem.
We are working with 75 partners to apply AI to healthcare. This number is growing every month. Our partners include a variety of medical centers, medical imaging companies, research institutions, healthcare startups and healthcare service providers.
Many partners will attend the annual meeting of the North American Radiological Society in Chicago this week. In addition to demonstrating the results of our cooperation at the annual meeting, we will also announce several important development processes:
Released the NVIDIA Clara Software Development Kit (SDK)
Announcement of Migration Learning Toolkit for Medical Imaging and AI Auxiliary Annotation SDK
Ohio State University is working with NVIDIA to create the first on-campus AI market with the NVIDIA Clara platform
The National Institutes of Health is working with NVIDIA to bring AI tools to clinical trials
Intelligent Imaging: ClaraSDK is now available
With the latest release of ClaraSDK, developers can easily deploy AI, visualization or compute-intensive applications (such as image reconstruction) from any GPU platform they have.
NVIDIA GPUs have been playing a key role in medical imaging for more than a decade. Diagnostic image morphology relies on our GPU for real-time, state-of-the-art image reconstruction, including iterative reconstruction to reduce CT scan radiation dose, compressed sensing that shortens MRI scan time, and software beam that improves ultrasound image quality Forming.
In addition, AI can even further improve image acquisition. The imaging instrument needs to ensure that the highest quality images can be acquired through AI. Imaging companies such as Lian Ying, Fujifilm and Canon have deployed NVIDIA DGX supercomputers as AI infrastructure to accelerate enterprise AI development.
The Clara SDK is part of the open NVIDIA Clara platform, which enables the medical imaging industry to build and deploy advanced imaging applications and AI-enabled workflows.
The MGH&BWH Clinical Data Science Center has incorporated the NVIDIA Clara SDK into its AI deployment strategy. They have developed a model for detection of abdominal aortic aneurysms and are deploying them to the Nuance AI market based on NVIDIA Clara.
“If we want to benefit from the thousands of new AI applications we are developing, we need to develop a path to deployment in many clinical and imaging centers. This deployment path is to increase AI adoption rates in the field of radiology. Key,†said Mark Michalski, executive director of the MGH&BWH Clinical Data Science Center.
You can learn more about the ClaraSDK collection that includes GPU-accelerated software tools, libraries, AI engines, containers, and sample applications.
The radiology workflow requires thousands of algorithms
Changing the practice of radiology will require thousands of applications. In view of the need for AI applications and the need to adapt these applications to the organization's patients, machines and practices, more than 50 leading healthcare organizations (including MGH, BWH, National Institutes of Health, University of California, San Francisco, Ohio State University, Mayo Hospital and King's College London have invested in the NVIDIA DGX system to develop AI applications.
To improve the ability of the radiology industry to build and adapt AI applications, NVIDIA has announced two key technologies:
AI Auxiliary Annotation SDK: Enables radiologists to unlock data values ​​at a speed 10 times the traditional annotation method.
Migration Learning Kit for Medical Imaging: Allows doctors to customize and adjust AI applications based on patient conditions. This technology is critical because each radiology practice is unique and has unique instruments, protocols, and patient statistics.
“At Ohio State University, we understand the importance of these tools. Data stewardship is one of the main bottlenecks in the algorithm development lifecycle. In the medical imaging field, the data itself is complex, plus highly trained annotator availability. This is particularly true," said Luciano Prevedello, head of imaging information at the Wexner Medical Center at Ohio State University.
“The migration learning techniques used in the toolkit can significantly reduce the number of images required for training while avoiding degrading algorithm performance,†Prevedello continued. “This toolkit is more efficient and uses AI. Implementing the data management process for filing will open the door to a new era of algorithm development."
Ohio State University builds the first on-campus AI market
As a medical center and university with cutting-edge academic standards, Ohio State University's Wexner Medical Center is the first US partner to use the NVIDIA Clara platform to create an intra-campus AI clinical imaging market.
Ohio State University's AI market will enable radiologists to quickly apply deep learning and machine learning to their own workflows.
“The rapid application of artificial intelligence has opened up a good opportunity for medical imaging,†said Dr. Richard White, director of the radiology department at the Wexner Medical Center at Ohio State University. “By working with NVIDIA, we have streamlined AI. Integration into the workflow process, which will improve the patient's treatment."
Ohio State University will deploy deep learning and machine learning to improve the clinical response rate in emergencies such as detecting cerebral hemorrhage or coronary artery disease. These algorithms can be integrated into many clinical workflows, such as early warning systems in emergency departments, job schedule optimization in radiology laboratories, or diagnostic assistants in reading rooms.
In addition, this brings another benefit: By standardizing on the deployment platform, organizations can potentially share and integrate the best AI applications built by this fast-growing ecosystem.
National Institutes of Health introduces AI tools into clinical trials
NVIDIA is also working with the National Institutes of Health, which operates the nation's largest research hospital and conducts more than 1,600 trials a year.
NVIDIA will arrange for researchers and engineers to work with clinicians at the National Institutes of Health Clinical Center. Our initial collaborative project will focus on AI tools designed to simplify clinical trials of brain and liver cancer.
The joint development project will also focus on developing AI tools that combine imaging, genomics and clinical data to provide accurate care for cancer patients. We will do this through a data-centric, dedicated AI platform and deep learning-based image omics.
“If you want to apply powerful tools such as deep learning to the medical field, we need to form an interdisciplinary team that truly encompasses doctors, hospitals, and computer scientists, so that they can work together to harness the potential of computer models in medical imaging. Develop predictive imaging biomarkers," said Dr. Elizabeth Jones, director of the Department of Radiology and Imaging Sciences at the National Institutes of Health Clinical Center.
Currently, radiologists are also using the method of manually measuring tumors according to existing guidelines to determine cancer staging. In contrast, AI will change the process by automatically depicting and measuring tumors in a way that ordinary observers may not be aware of.
In addition, AI may also combine the use of data other than tumor size with other currently used staging criteria to improve the accuracy of cancer staging. The new imaging biomarkers discovered by AI can be used in clinical trials, allowing us to move closer to predictive and personal precision medicine.
To bring AI to the global radiology industry, we need to involve radiologists in patient-oriented algorithm creation and adjustment. It is also important that we provide these doctors with a standardized way to share and integrate these breakthroughs with colleagues, while also enabling them to conduct on-site data analysis with less regulatory or privacy risks.
Intelligent instruments and automated workflows have become a reality. NVIDIA is working with industry thought leaders to enable radiology to cross the AI ​​divide through the NVIDIA Clara platform.
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