Digital Twins of Human Lungs to Improve Ventilator Utilization
Project BreathEasy
Project BreathEasy is a collaboration between engineers, scientists, doctors, and simulation experts to create actionable Digital Twin solutions to assist doctors making critical ventilation decisions during the COVID-19 global outbreak.
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About Project BreathEasy
Project BreathEasy was begun in March 2020 by OnScale, a Cloud Simulation company, and LEXMA, and advanced computational fluid dynamics (CFD) solver company. The two companies prepared early proofs-of-concept (POCs) for Digital Twins of human lungs using real, anonymized COVID-19 patient data like CT and xray imaging.
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OnScale and LEXMA are actively seeking additional partners from the scientific, engineering, and medical communities to support research and development of Digital Twin solutions. The R&D we do during this crisis will better prepare humanity for the next pandemic.
A Digital Twin is a real-time digital representation of a physical system. Digital Twins are built using 3D data (e.g. from CT or xray images, in the case of a human organ), advanced 3D simulation, real-time physical data from sensors, and artificial intelligence.
For many years, Digital Twins of industrial equipment like jet engines, wind turbines, and machine tools have been used to inform predictive maintenance, which reduces the cost and risk of legacy preventative maintenance (maintenance on a fixed schedule) or reactive maintenance (maintenance when something breaks) processes.
Today, these same Digital Twin techniques can be applied to the Human Body. For example, doctors can use Digital Twins of human lungs of COVID-19 patients to make more informed decisions about ventilator settings and ventilation time for a patient. Ideally, better decision making will lead to better patient outcomes and better utilization of limited ventilator resources.
Partners
Project BreathEasy was formed by OnScale and LEXMA.
We are actively seeking additional partners to support this very important research and development.