Technology | Remote Viewing Systems | December 13, 2017

AI Visualize Inc. Debuts Artificial Intelligence Cloud Image Visualization Platform at RSNA 2017

Enterprise solution transmits detailed 2-D or 3-D imaging renderings to users on any internet-enabled device

AI Visualize Inc. Debuts Artificial Intelligence Cloud Image Visualization Platform at RSNA 2017

December 13, 2017 — AI Visualize Inc. debuted its new cloud-based artificial intelligence (AI)-based image analysis and viewing platform at the 2017 Radiological Society of North America (RSNA) Annual Meeting, Nov. 26-Dec. 1 in Chicago. The technology relies on evolution and deep learning algorithms to assess imaging data for diagnostically valuable information not readily apparent through conventional analysis, and transmits detailed 3-D/2-D renderings to users on any internet-enabled device. The technology assists physicians in making the most of today’s data-intensive images for more accurate and precise diagnoses. The advanced interactive platform supports X-ray, ultrasound, computed tomography (CT), magnetic resonance imaging (MRI), 3-D tomosynthesis and digital pathology images, and provides a full range of relevant virtual image analysis tools.

Eliminating the need to communicate large datasets over the internet, the new interactive AI platform processes images in the AI VoXcell cloud using powerful graphics processing unit (GPU) technology. It streams relevant information and imaging tools to users, wherever located, in real time. The same AI technology that processes images also overcomes internet bandwidth and latency limitations by using predictive buffering to provide fast transmission rates over existing communications lines.

Server-side rendering eliminates the need for powerful and costly onsite graphics processors, supporting off-the-shelf supercomputing infrastructure to make the solution scalable and affordable. The AI Visualize technology is covered by 11 international patents. It is U.S. Food and Drug Administration (FDA)-cleared and HIPAA-compliant, supporting high levels of security.

Other capabilities and features of the AI Cloud Visualization Platform include:

Visualization

  • Transmitting a volume visualization dataset (e.g. a 3-D dataset) or a medical imaging dataset, including non-3-D datasets such as for pathology, for secure storage on a remote database via a security device;
  • Utilizing servers to create virtual views of the dataset per requests from a web/internet-based client applications;
  • Accessing the servers with the client application via a security device;
  • Using the client application to display virtual views of the remote dataset generated remotely by the servers;
  • Virtual views are transmitted from the server to the client device as a compressed video stream; and
  • Ensuring the database, storage medium and servers are at a physically secured site.

Cloud bandwidth and latency

  • Display of higher quality parameter frames after completing display of the lower quality parameter frames;
  • Predictive buffering of the virtual views transmitted to client device;
  • Delay of frame requests from the client device to the server in accordance with the latency; and
  • Evolving compression-parameters based on an algorithm that optimizes the compression to  maintain image quality (compression using artificial intelligence)

Cloud visualization artificial intelligence

  • Machine learning algorithms may be applied to the processing of volume visualization datasets;
  • A pattern recognizer determines whether the central application server flags an image view as normal or abnormal and identifies the anomaly in the view;
  • A neural network model determines whether the transfer function-generated image matches the prototype image; and
  • The neural network employs a convolution neural network model.

For more information: www.aivisualize.com

Related Content

News | PACS Accessories | February 22, 2018
The RamSoft team will showcase radiology solutions to help users cut costs and save time at the 2018 Healthcare...
Mirada Medical Releases DLCExpert for Radiotherapy Treatment Planning
Technology | Treatment Planning | February 22, 2018
February 22, 2018 — U.K.-based medical imaging software provider Mirada Medical has released DLCExpert, the first com
Sponsored Content | Videos | Pediatric Imaging | February 22, 2018
FUJIFILM Medical Systems U.S.A., Inc. and FUJIFILM SonoSite Inc., are offering a full-suite pediatric solutions...
ECR 2018 Spotlights Artificial Intelligence and Current Radiology Trends
News | Interventional Radiology | February 21, 2018
The European Congress of Radiology (ECR) announced the theme of its 2018 annual meeting, Feb. 28-March 4 in Vienna...
Sponsored Content | Videos | Flat Panel Displays | February 21, 2018
This 5 megapixel, high-brightness color monitor has the high-definition display necessary for breast imaging.
Aidoc Introduces AI Solution for Whole-Body CT Scan Analysis
News | Artificial Intelligence | February 20, 2018
Deep learning startup company Aidoc announced what it calls the world’s first and only comprehensive, full-body...
Logicalis White Paper Aims to Optimize Every Healthcare IT Dollar
News | Information Technology | February 20, 2018
February 20, 2018 – Digital transformation is happening in all industries, and it will happen in healthcare whether h
A brain MRI. Gadolinium contrast agents (GBCAs) are partly retained in the brain, raising safety concerns.
Feature | Magnetic Resonance Imaging (MRI) | February 16, 2018 | Dave Fornell
One of the biggest concerns in radiology in recent years is the safety of gadolinium-based contrast agents (GBCAs) us
Arterys Receives First FDA Clearance for Oncology Imaging Suite With Deep Learning
Technology | Artificial Intelligence | February 15, 2018
Arterys Inc. announced its fifth 510(k) clearance from the U.S. Food and Drug Administration (FDA) for the Arterys...
Sponsored Content | Videos | Imaging | February 15, 2018
David Widmann, president and CEO of Konica Minolta, looks at what the future of healthcare can bring to its customers,...
Overlay Init