Using Azure Health Data Services & Softneta MedDream viewer for medical imaging in the cloud

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This blog has been authored by Matthew Mitrik, Principal Program Manager Health Data + AI at Microsoft

 

 

 

 

 

This blog is in collaboration with our partners and customers leveraging Azure Health Data Services. Azure Health Data Services, a platform as a service (PaaS) offering designed to support Protected Health Information (PHI) in the cloud, is a new way of working with unified data—providing care teams with a platform to support both transactional and analytical workloads from the same data store and enabling cloud computing to transform how we develop and deliver AI across the healthcare ecosystem.

 

The imaging community is accustomed to continual technological change. In the past decades, Radiology has seen not only the wide scale digitization of medical imaging but also the near universal adoption of image management technology. Today, with widely available utility scale compute and storage in the cloud, we stand at the precipice of another big technological shift – driving personalized care and better diagnosis through data driven radiological innovations and implementation of advances in machine learning and Artificial Intelligence (AI).

 

 

 

The challenge however, still remains that migration of medical imaging data to the cloud is a hard and daunting task. Particularly so for medical imaging with its massive data sets. It involves not only dealing with existing legacy systems, but analysis of cost and time it will take to complete the migration. Organizations are faced with this gargantuan task and don’t quite know where to start – what should their first step be? What are the data estate considerations they need to make while addressing this challenge. Are there services that they must have that are geared towards medical imaging and its unique needs?

 

 

 

 

 

A few questions that organizations need to answer while thinking about migrating their data to the cloud include:

 

 

 

1. How big is my data estate? What portions of my data should be moved to the cloud first?
   
2. What kind of medical imaging data do I need to migrate?
   
3. What are my existing systems, how do they interoperate, and how does the cloud affect the interoperability?
   
4. What are my disaster recovery and business continuity plans?
   

 

 

 

Microsoft’s Azure Health Data Services aims to support organizations as they make this transition to the cloud and help combine clinical, imaging, and MedTech data using global interoperability standards like Fast Healthcare Interoperability Resources (FHIR®) and Digital Information Communication in Medicine (DICOM). The DICOM service within Azure Health Data Services allows standards-based communication with any DICOMweb™ enabled systems such as medical imaging systems, vendor-neutral archives (VNAs), picture archiving, and communication systems (PACS), etc. The goal is to fully leverage the power of the cloud infrastructures for medical images, creating a service that is fast, highly reliable, scalable, and designed for security.

 

 

 

One critical component in any cloud hosted medical imaging solution is a Zeo Footprint (ZFP) viewer enabling clinicians to view their diagnostic imaging on any device with an internet connection. Softneta has created the MedDream viewer which is easy to deploy and integrates perfectly into Azure Health Data Services making it a great choice for organizations. MedDream’s availability as a container provides a great amount of flexibility when deploying the zero-footprint (ZFP) viewer. Whether you choose to host your own Kubernetes cluster or prefer to host containers using Azure Container Instances, MedDream is simple to deploy. Native integration with Azure Health Data Services, using DICOMWeb standard APIs and Azure AD, keeps the MedDream configuration simple and easy to manage without sacrificing security. Once configured, the end-user experience is seamless, allowing users to focus on the imaging, not the imaging infrastructure.

 

 

 

“Softneta’s MedDream is an extremely versatile, performant, and flexible medical imaging viewing and manipulation application. We have used it as an integral component of various clinical workflow and data science solutions and love the communication APIs, the low impact server-side footprint, and deployment and hosting flexibility. We also are impressed by the responsiveness and collaborative nature of the Softneta team. The combination of MedDream and the Azure Health Services will be a powerful capability for digital health innovators needing best-in-class performance, stability, and scale.” John F. Kalafut, PhD, CEO & Co-Founder of Asher Orion Group.

 

 

 

MedDream is architected for flexibility and contains a breadth of capabilities to connect and scale. All common image and study manipulation capabilities (measurements, orientation, etc.) are supported natively out of the box. MedDream is extensible- use it as a standalone web application, integrate it into your web application as an iFrame using windows post API or incorporate as components in a REACT or Angular framework application. The rendering and display of the imaging data is done via webGL thus making use of the power in every device and desktop browser. Advances in web assembly and underlying technology powering WebGL are reflected in the MedDream application because Softneta maintains one code base and pushes updates to all users at each major release.

 

 

 

To see how easy it is to deploy and integrate MedDream, register for the Softneta and Azure Health Data Services webinar:

 

Integrating MedDream Viewer with Azure Health Data Services

 

Thursday, April 6th at 6:00 pm EST – Register Here

 

 

In this interactive webinar, you’ll see MedDream in action and learn to install and configure the MedDream View Docker for Azure Health Data Service. You’ll see a demonstration of a simulated hospital network environment with DICOM service and the MedDream Viewer, as well as the workflow with PACS, DICOM service and the MedDream Viewer.

 

 

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