AI Platform Upgrades CT, Ultrasound, and Analytics Solutions

For patients, the partnership aims to drive lower radiation doses, speed up exam times, and provide a higher quality of medical imaging.

The first device to benefit from the collaboration is the Revolution Frontier computed tomography (CT) system, which processes images twice as fast as its predecessor by using Nvidia’s AI computing platform. The increased processing power is expected to deliver better clinical outcomes in liver lesion detection and kidney lesion characterization, potentially reducing the need for unnecessary follow-ups, benefiting patients with compromised renal function, and reducing non-interpretable scans with the assistance of Gemstone Spectral Imaging Metal Artefact Reduction (GSI MAR).

Another device that will benefit is the GE Healthcare Vivid E95 4D Ultrasound system, which will use the Nvidia graphics processing unit (GPU) to provide fast and accurate visualization and quantification of ultrasound imaging while streamlining workflows across the cSound imaging platform. The Nvidia Quadro GPU will accelerate reconstruction and visualization of blood flow and improve two-dimensional (2D) and four-dimensional (4D) imaging for echo lab and interventional deployments.

“Our partnership with GE Healthcare brings together great expertise in medical instruments and AI to create a new generation of intelligent instruments that can dramatically improve patient care,” said Jensen Huang, founder and CEO of NVIDIA.

“Healthcare is changing at remarkable speed, and the technologies that will transform the industry should reflect that pace,” said Kieran Murphy, President and CEO of GE Healthcare. “By partnering with NVIDIA, GE Healthcare will be able to deliver devices of the future – intelligent machines capable of empowering providers to improve the speed and accuracy of diagnoses for patients around the world.”

Modules of the new GE Healthcare Applied Intelligence analytics platform will also use Nvidia GPUs, the Nvidia CUDA parallel computing platform and application programming interface (API) model, and the NVIDIA GPU Cloud container registry to accelerate the creation, deployment, and consumption of deep learning algorithms in new healthcare analytic applications, which will be seamlessly integrated into various clinical and operational workflows.