Precision therapy based on personalized digital twins

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Mission

Precision medicine through high-fidelity digital twins

Personalized digital twins

We believe in major advances in the healthcare sector through fusing physical and physiological knowledge with data and machine learning into predictive computational models. These software-based digital twins will not only transform the development processes for drugs and medical devices, but also enable a new class of diagnostic and monitoring tools.

Clinical Decision Support

We want to provide patients suffering from Acute Respiratory Distress Syndrome (ARDS) with individually tailored accurate mechanical ventilation settings to improve their odds of survival and recovery.

Protective precision ventilation therapy for ARDS patients

Local mechanical overload of the lungs due to suboptimal ventilator settings is a major contributor to the high mortality in patients suffering from Acute Respiratory Distress Syndrome (ARDS). Our technology enables us to provide the best possible protective ventilation protocol for each individual patient, reducing ventilator inflicted lung damage.

Predictive & physics-based

Combining a CT-scan of the patient's lungs with in-depth physiological knowledge and engineering, and physics-based algorithms we create highly accurate digital twins of the human lungs. Using our technology, we provide unprecedented insights and tailored precision treatment options.

Quantitative & Functional Imaging

Learn more from CT-scans through quantitative analysis and functional evaluations.

Segmentation & Image-based biomarkers

Automatically extract valuable information from CT scans with the use of the latest AI and image analysis methods. Create patient-specific segmentations of the lung or identify pathologies to improve standard medical imaging procedures and evaluate image-based biomarkers.

Quantitative, functional imaging

Automated analysis in combination with quantitative intelligence transform medical images into rich visualizations with quantitative reports to improve diagnostics and provide better care faster.

Perspective: In-Silico Trials

Pulmonary drug delivery is a challenging task that depends on the interplay of the drug, the aerosol, the inhaler device and the patient's breathing.

Major benefits of virtual patients
  • Time savings
  • Cost reduction
  • No health risk for human participants
  • 100% control over study protocol and conduction
  • 100% reproducibility of results
Preclinical R&D

Valuable insights from rapid and inexpensive testing of designs and parameters will accelerate R&D projects. De-risk the following clinical trials by reliable predictions based on early studies with virtual patients.

Clinical trials

Reduce the number of human participants in a clinical trial by including virtual patients. Regulatory agencies such as the FDA are already promoting the unprecedented opportunities of in-silico trials. Assessing safety and efficacy in virtual patients reduces the risk for study participants and for patients after the approval.

Team

Ebenbuild's team is comprised of experts in biomedical engineering, machine learning, and software development on a mission to transform healthcare through digital technology.

Dr. Kei Müller
CEO & Co-Founder
Dr. Jonas Biehler
CTO & Co-Founder
Dr. Karl-Robert Wichmann
Chief Software Architect & Co-Founder
Prof. Wolfgang Wall
Chief Scientific Advisor & Co-Founder
Nina Pischke, M.Sc.
Deep Learning Engineer
Marie Brei, M.Sc.
Machine Learning Engineer
Dr. Maximilian Grill
Senior Research Engineer
Jakob Richter, B.Sc.
Student Software Engineer

News

Project BEATE

Our R&D project BEATE is featured in the 2020 project gallery of the Federal Ministry of Education and Research

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Ebenbuild in Bild der Wissenschaft

The latest cover story of the German magazine Bild der Wissenschaft is about ICUs and what it takes for physicians, nurses, and patients to win the fight for lives. Ebenbuild and our #digitaltwin technology are part of the feature

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Ebenbuild featured on DW

DW did a feature with audEERING GmbH, mediaire, and Ebenbuild on how machine learning will advance diagnosis and patient care.

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Ebenbuild on t3n

Nice analysis by t3n Magazin about startups - amongst others Ebenbuild - which bring digital technologies into hospitals to improve patient care.

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Partners & Supporters

Media

Our work has been featured by various media outlets.

Awards

The team and business concept have received several high-profile and competitive awards.

Funding

Ebenbuild is currently funded through the EXIST-Forschungstranfer program of the Ministry of Economic Affairs and Energy and the Start-MTI program of the Ministry of Education and Research.

Contact Us

If you have any questions, please reach out.