Ebenbuild recently partnered with Daylight for a UX-Design project. Take a look at their case study
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Precision medicine through high-fidelity 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.
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.
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.
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.
Learn more from CT-scans through quantitative analysis and functional evaluations.
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.
Automated analysis in combination with quantitative intelligence transform medical images into rich visualizations with quantitative reports to improve diagnostics and provide better care faster.
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.
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.
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.
Ebenbuild's team is comprised of experts in biomedical engineering, machine learning, and software development on a mission to transform healthcare through digital technology.
Ebenbuild recently partnered with Daylight for a UX-Design project. Take a look at their case study
Read more
Our R&D project BEATE is featured in the 2020 project gallery of the Federal Ministry of Education and Research
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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 is listed as one of the most promising AI-startups in Germany in the annual Startup Landscape by appliedAI Initiative
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DW did a feature with audEERING GmbH, mediaire, and Ebenbuild on how machine learning will advance diagnosis and patient care.
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Nice analysis by t3n Magazin about startups - amongst others Ebenbuild - which bring digital technologies into hospitals to improve patient care.
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Our work has been featured by various media outlets.
The team and business concept have received several high-profile and competitive awards.
Ebenbuild is currently funded through the EXIST-Forschungstranfer program of the
If you have any questions, please reach out.