Whether facilitating cancer screenings, cutting down on false positives, or improving tumor identification and treatment planning, AI is a powerful agent for healthcare innovation and acceleration.
Yet, despite its promise, integrating AI into actual solutions can challenge many IT organizations.
The Netherlands Cancer Institute (NKI), one of the world's top-rated cancer research and treatment centers, is using the NVIDIA AI Enterprise software suite to test AI workloads on higher-precision 3D cancer scans than are commonly used today.
NKI's AI model was previously trained on lower-resolution images. But with the higher memory capacity offered by NVIDIA AI Enterprise, its researchers could instead use high-resolution images for training. This improvement helps clinicians better target the size and location of a tumor every time a patient receives treatment.
The NVIDIA AI Enterprise suite that NKI deployed is designed to optimize the development and deployment of AI. It's certified and supported by NVIDIA to enable hospitals, researchers and IT professionals to run AI workloads on mainstream servers with VMware vSphere in their on-prem data centers and private clouds.
Delivering treatments on virtualized infrastructure means hospitals and research institutions can use the same tools they already work with on existing applications. This helps maximize their investments while making innovations in care more affordable and accessible.
NKI used an AI model to better reconstruct a Cone Beam Computed Tomography (CBCT) thoracic image, resulting in clearer image quality compared to conventional methods. Speeding Breakthroughs in Healthcare Research NKI had gotten off to a quick start with its project on NVIDIA AI Enterprise by using NVIDIA LaunchPad.
The LaunchPad program provides immediate access to optimized software running on accelerated infrastructure to help customers prototype and test data science and AI workloads. This month, the program was extended to nine Equinix locations worldwide.
The NVIDIA AI Enterprise software suite, available in LaunchPad, makes it possible to run advanced AI workloads on mainstream accelerated servers with VMware vSphere, including systems from Dell Technologies, Hewlett Packard Enterprise, Lenovo and many others.
Rhino Health, a federated learning platform powered by NVIDIA FLARE, is available today through NVIDIA AI Enterprise, making it easy for any hospital to leverage Federated learning for AI development and validation. Other organizations, like The American College of Radiology's AI LAB, are also planning to use the NVIDIA AI Enterprise software.
Researchers at NKI used NVIDIA AI Enterprise, running on the HPE Synergy, a composable software system from Hewlett Packard Enterprise, to build deep learning models by combining the massive 2D and 3D data sources and AI to pinpoint the location of tumors before each radiotherapy treatment session.
Doctors could use this solution as an alternative to CT scans on day of treatment to optimize the treatment plan to validate the radiotherapy plan, said Jonas Teuwen, group leader at the Netherlands Cancer Institute.
Using NVIDIA AI Enterprise, Teuwen's team in Amsterdam ran their workloads on NVIDIA A100 80GB GPUs in a server hosted in Silicon Valley. Their convolutional neural network was built in less than three months and was trained on less than 300 clinical lung CT scans that were then reconstructed and generalized to head and neck data.
In the future, NKI researchers also hope to translate this work to potential use cases in interventional radiology to repair arteries in cardiac surgeries and dental surgery implants.
Accelerating Hospital AI Deployment With NVIDIA AI Enterprise NVIDIA AI Enterprise simplifies the AI rollout experience for organizations who host a variety of healthcare and operations applications on virtualized infrastructure. It enables IT administrators to run AI applications like Vyasa and iCAD alongside core hospital applications, streamlining the workflow in an environment they're already familiar with.
Compute resources can be adjusted with just a few clicks, giving hospitals the ability to transform care for both patients and healthcare providers.
Vyasa, a provider of deep learning analysis tools for healthcare and life sciences, uses NVIDIA AI Enterprise to build applications that can search unstructured content such as patient care records. With the software, Vyasa can develop their deep learning applications faster and help dive through unstructured data and PDFs to assess which patients are at a higher risk. It identifies those who haven't been in for a check-up in more than a year, and can refine for additional risk factors like age and race.
NVIDIA AI Enterprise has reduced our deployment times by half thanks to rapid provisioning of platform requirements that eliminate the need to manually download and integrate software packages, said Frans Lawaetz, CIO at Vyasa.
Radiologists use iCAD's innovative ProFound AI software to assist with reading mammograms. These AI solutions help identify cancer earlier, categorize breast density, and accurately assess short-term personalized breast cancer risk based on each woman's screening mammogram. Running advanced workloads with VMware vSphere is important for iCAD's healthcare customers as they can easily integrate their data intensive applications into any hospital infrastructure.
A host of other software makers, like the American College of Radiology's AI LAB and Rhino Health, with its federated learning platform, have begun validating their software on NVIDIA AI Enterprise to ease deployment by integrating into a common healthcare IT infrastructure.
The ability for NVIDIA AI Enterprise to unify the data center for healthcare organizations has sparked the creation of an ec
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Whether facilitating cancer screenings, cutting down on false positives, or improving tumor identification and treatment planning, AI is a powerful agent for healthcare innovation and acceleration.
