Ebenbuild to Receive Public Funding Under UBIC Consortium for Digital Lung Twin Project

EbenbuildEbenbuild, a company developing personalized, AI-driven virtual lungs to support clinical decisions and digital clinical trials, announced its participation in the research project Personalized Lung Twins for the Treatment of Acute Respiratory Distress Syndrome (UBIC) in collaboration with the University Hospital Schleswig-Holstein, Augsburg University Hospital, University Hospital Carl Gustav Carus Dresden, University Hospital RWTH Aachen and University Medical Center Mannheim.

In total, the consortium will receive up to Euro 1.8 million in funding from the German Federal Ministry of Education and Research (Bundesministerium für Bildung und Forschung, BMBF) for its project "Personalized Lung Twins for the Treatment of Acute Respiratory Distress Syndrome (UBIC)".

As part of the UBIC consortium, Ebenbuild will receive up to Euro 900.000 to develop novel AI and simulation technologies for modeling and researching heterogeneously damaged lungs and to improve digitally assisted decision-making in intensive care.

Ebenbuild develops personalized lung simulation models based on patient-specific data. These so-called digital lung twins are used to improve treatment outcomes for ICU patients who are on ventilators and suffering from acute respiratory distress syndrome, or ARDS in short. ARDS is a life-threatening complication with many causes such as trauma, sepsis, or severe illnesses, e.g., COVID-19. As a result of fluid leakage into the lungs, breathing becomes difficult, and oxygen cannot get into the body.

Adequate treatment is vitally important, but expertise is usually available in specialist centers only and extremely time-consuming. Despite advances in diagnosis and intensive medical treatment, ARDS is still detected too rarely and is therefore undertreated, representing a major global problem. As a result, around 40 percent of ARDS patients die. Since the start of the Covid pandemic, these figures have increased even further, with over 80 percent of deaths following a Covid infection attributable to ARDS.

"We are delighted about the funding and our participation in the project," said Dr. Kei Müller, CEO and co-founder of Ebenbuild. "It's an important step towards reaching our goal to provide clinics around the world with sophisticated decision support to test various scenarios for a virtual evaluation of available therapeutic strategies. Our digital lung twin offers unprecedented medical support by mimicking individual lungs. This allows medical doctors to assess various treatment options under real-life conditions, improve the ventilation quality and ultimately save patients´ lives."

"The complex, heterogeneous patient data provided by our clinical cooperation partners will for the first time enable the analysis and evaluation of ARDS based on real patient data," said Dr. Jonas Biehler, CTO, and co-founder of Ebenbuild. "This will help us to develop and train new data science and deep learning algorithms, enable the further automation of modeling and personalization processes and support our development of a scalable AI and simulation technology for an ubiquitous clinical use."

For further information, please visit:
https://www.ebenbuild.com

About Ebenbuild

Ebenbuild is a digital health tech pioneer developing precise, patient-specific simulation models of lungs based on patient data, so-called digital twins of the lungs. Its digital toolset is based on physics-based simulation, AI, and data science and is designed to support decision makers in the life sciences industry, clinicians, and physicians. Ebenbuild's approach provides a better understanding of respiratory diseases and individual pathophysiology enabling a new class of products and services ranging from in silico trials to accelerate and de-risk the development of inhaled drugs to personalized treatment options that improve the chances of survival and recovery in millions of cases of respiratory conditions such as Acute Respiratory Distress Syndrome (ARDS) each year.

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