For a patient with acute respiratory distress syndrome (ARDS) in the ICU, the ventilator is a lifeline that can also cause harm. Standard settings are a blunt instrument, a one-size-fits-all approach that risks further injuring fragile, inflamed lung tissue. A Munich-based team of computational engineers and scientists believes the answer lies in the physics-informed digital twin built from CT scans.
Ebenbuild, founded in 2019, is developing software that converts patient CT scans and clinical data into high-fidelity, AI-enhanced digital replicas of the lungs. These are simulation models that combine computational fluid dynamics and tissue mechanics to predict airflow, applied forces, and tissue expansion within the organ. The intended output is a set of personalized ventilation parameters, a recommendation for the ICU clinician on how to adjust the machine to minimize stress on that specific patient’s lungs.
A dual-market wedge in respiratory care
ICU decision support
The primary application is in the intensive care unit for patients with ARDS, acting as a clinical decision support tool to move ventilator management from population-based protocols to patient-specific optimization.
Pharmaceutical R&D
The second application is in preclinical drug development. Pharmaceutical companies can use cohorts of these virtual lungs to run in-silico trials, simulating how inhaled therapies for conditions like pulmonary fibrosis are transported and deposited throughout the respiratory tract.
Validation and the European backing
Ebenbuild’s technology was recently validated in a study published in Nature Communications Medicine, where its digital twin accurately predicted inhaled drug deposition against gold-standard 3D SPECT/CT imaging data from a clinical trial. The company has secured a mix of seed funding and significant grants.
| Metric | Value |
|---|---|
| 2022 Seed | €2.5M |
| 2025 EIC Grant | €2.3M |
| BMBF Grant | €0.9M |
Beyond the €2.5 million seed round led by Bayern Kapital in 2022, the startup has won a €2.3 million grant from the European Innovation Council and up to €900,000 from the German Federal Ministry of Education and Research. This public backing, totaling over €5.7 million, signals a strong vote of confidence in the technology’s scientific merit.
The academic engine behind the startup
The company was spun out directly from research at the Technical University of Munich (TUM). The founding team includes CEO Dr. Kei Wieland Müller, CTO Dr. Jonas Biehler, Co-Founder Prof. Dr. Wolfgang A. Wall, and VP Engineering Karl-Robert Wichmann. The presence of a sitting full professor (Wall) as a co-founder underscores the depth of the scientific underpinnings.
Navigating the path to the bedside
The company’s long-term roadmap explicitly targets clinical-grade decision support systems, which will require regulatory approval as a medical device. This process is lengthy, expensive, and demands robust clinical trials demonstrating not just predictive accuracy, but improved patient outcomes.
The next twelve months
With its recent grant funding secured, Ebenbuild’s immediate focus will be on advancing its two product tracks. For the pharmaceutical segment, expect more announced partnerships with drug developers leveraging the validated in-silico trial capabilities. For the clinical track, the next milestones will be initiating the necessary clinical validation studies to support a regulatory filing and engaging with early pilot ICU sites.