Siemens Healthineers to co-lead EU UMBRELLA project aimed at improving stroke management

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Siemens Healthineers has announced today that it will be joining forces with more than 20 industry and public partners—including seven leading stroke hospitals—to improve stroke management for patients across Europe. With a total volume of €26.9 million, the five-year ‘UMBRELLA’ project is being funded partly by the Innovative Health Initiative (IHI), a public-private partnership for health research and innovation between the European Union and Europe’s life science industries, and partly by industry partners participating in the consortium.

UMBRELLA is set to be co-led by Vall d’Hebron Research Institute (Barcelona, Spain) and Siemens Healthineers, and includes hospitals in Belgium, Germany, Italy, The Netherlands, Spain and Switzerland. In a recently issued press release intended for European audiences, Siemens Healthineers states that—with an investment of €6.7 million—it is the largest industry contributor involved in the project.

The release also notes that two of the company’s own developments are key to this project. The first one, a novel stroke connectivity platform, is designed to bridge gaps along the acute stroke pathway and to digitalise patient data points. Secondly, a federated data and learning platform will enable researchers to create and validate algorithms on a large number of datasets in a secure way.

UMBRELLA’s aims are to improve and standardise stroke management protocols, validate them across participating clinical centres, and implement them in the European guidelines for the benefit of all stroke patients. In addition, the project will focus on creating and validating artificial intelligence (AI)-supported algorithms based on real-world data for improved diagnosis, reduced time-to-treatment and the prevention of long-term damage.

“UMBRELLA is a remarkable example of how technology and collaboration can improve patient outcomes boosted by cutting-edge technologies. This is critical to addressing major unmet health needs,” said Peter Schardt, chief technology officer of Siemens Healthineers. “Partnerships like this foster the European innovation ecosystem and provide a significant opportunity to achieve results that would not be possible for any single organisation working alone.”

“Together with leading clinical partners, we want to optimise stroke treatment and limit recurrence through digitalisation and advanced imaging,” added Carsten Bertram, head of Advanced Therapies at Siemens Healthineers. “This project is an important milestone towards our mission to lower the global burden of stroke.”

According to Siemens Healthineers, UMBRELLA will be able to utilise a novel stroke connectivity platform provided by the company to facilitate communication and help break down existing data silos. Today, these exist between paramedics, and primary and comprehensive stroke hospitals, causing treatment delays for acute stroke patients. The platform will bridge gaps along the pathway, and allow for streamlined communication and a smooth exchange of information—such as patient demographics, vital signs, stroke scale results or computed tomography (CT) images. It will also enable digitalisation of a combination of patient data points that are not kept in stroke registries today, the release adds.

Siemens Healthineers’ federated data and learning platform, on the other hand, will help to establish data repositories with harmonised, real-world data from the participating hospitals. A communal learning infrastructure will be utilised for UMBRELLA, to allow for decentralised training and validation of AI-based algorithms on the federated data. This avoids the exchange of actual patient data amongst partners, fulfilling regional data compliance requirements, the release continues. The platform will enable researchers to formulate clinical hypotheses, and create and validate algorithms on a large number of harmonised, pan-European datasets—for example, for fast and precise stroke diagnosis. It will also allow personalised treatment strategies, and outcome and recurrence prediction models, to be built, the release concludes.


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