OnScale Launches Project BreathEasy: Digital Twins of Lungs to Improve COVID-19 Patients Outcomes

OnScale, the leader in Cloud Engineering Simulation, announces Project BreathEasy, a consortium of multiphysics FEA/CFD vendors, medical device manufacturers, engineers, and doctors from around the world who are developing digital twins of the lungs of COVID-19 patients to help doctors improve patient outcomes and optimize use of limited ventilator resources in major outbreak areas.

OnScale, the leader in Cloud Engineering Simulation, announces Project BreathEasy, a consortium of multiphysics FEA/CFD vendors, medical device manufacturers, engineers, and doctors from around the world who are developing digital twins of the lungs of COVID-19 patients to help doctors improve patient outcomes and optimize use of limited ventilator resources in major outbreak areas.

Latest COVID-19 outbreak predictions estimate that cases will far exceed available ventilator resources by 10 times or more. COVID-19 patients die from acute respiratory distress syndrome (ARDS), and with limited availability of ventilators in the US, maximizing per-patient utility of ventilators will be critical to saving lives. Doctors currently rely on textbook predictions of ventilator requirements, but more accurate predictions will maximize ventilator utility. Even a 10% improvement may save thousands of lives.

OnScale and LEXMA, a leading provider of advanced fluid flow and biomechanical simulation technology, have partnered to create patient-specific digital twins that may accurately predict oxygen and blood flow in a patient’s lungs, helping doctors make critical decisions about ventilator and intubation requirements for COVID-19 patients.

Each digital twin is patient-specific and built from a combination of medical images (for example from CT scans and X-rays) and thousands of simulations of lung airflow and blood flow using the LEXMA Moebius fluid dynamics solver running on OnScale’s Cloud Simulation platform. Digital twins are updated with real-time patient data. AI trained on simulated and measured patient data is used to make fast and accurate predictions of oxygen and blood flow throughout the ventilation and intubation process.

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