The startup develops a digital twin-based precision therapeutic technique that creates personalized protective ventilation protocols for patients with acute respiratory distress syndrome. By utilizing predictive computational models, the platform aims to minimize ventilator-induced lung damage and improve patient survival rates during mechanical ventilation.
Funding
$3.1M raised to dateRaised to date based on public sources. This may differ from the amount the company actually raised and is based only on what is publicly available on the internet.
Founders
Product
Problem
Patients with Acute Respiratory Distress Syndrome (ARDS) require mechanical ventilation, a process that can inadvertently cause further lung damage due to the difficulty in precisely adjusting ventilator settings for individual patients. Current methods for estimating the mechanical effects of ventilation on the lungs are indirect and qualitative, making optimal ventilation a rare expertise and contributing to high mortality and long-term morbidity.
Solution
Ebenbuild offers a digital twin platform that creates patient-specific, predictive models of the lungs to optimize ventilation strategies and drug delivery. By extracting patient-specific anatomy and pathologies from routine imaging, Ebenbuild's technology models the interplay between breathing, airflow, and tissue mechanics. This enables systematic assessment of lung-ventilation interaction through biomechanical quantities derived from physics-based lung models. The platform provides actionable health intelligence to clinicians, establishing a causal connection between ventilator settings and their effect on the individual patient’s lung, with the goal of improving patient outcomes and provider performance. Ebenbuild's digital twins also facilitate in silico trials to accelerate and de-risk the development of inhaled drugs by simulating drug deposition in diseased human lungs.
Target Audience
Ebenbuild targets clinicians and physicians treating patients with ARDS, as well as pharmaceutical companies developing inhaled drugs, and healthcare systems seeking to improve patient outcomes and reduce costs associated with mechanical ventilation.
Features
- Patient-specific digital twins of the lungs created from routine imaging data
- Physics-based simulation and AI techniques to model lung mechanics and airflow
- Prediction of aerosol deposition for inhaled drug delivery
- Biomechanical assessment of lung-ventilation interaction
- Actionable intelligence reports for clinicians to optimize ventilator settings
- In silico trials for de-risking and accelerating inhaled drug development
- Digital cohorts tailored to specific use cases