Project Details
Description
ECMOPT aims at surpassing the current standards in veno-venous Extra
Corporeal Membrane Oxygenation (VV-ECMO), by the introduction of an optimization paradigm. VV-ECMO is a well-established procedure used in
Intensive Care Units (ICU) to treat patients with pulmonary failure. The
blood of the patient is drained via a cannula positioned in the inferior vena
cava, oxygenated and reinserted via another cannula located in the
superior vena cava. VV-ECMO is the standard procedure in the case of
Acute Respiratory Distress Syndrome (ARDS), and is currently used for
Covid-19 patients in ICU. Still, its efficacy is very limited. The optimization
of the procedure is an open question in the field of Intensive Care Medicine,
but the consensus is that its shortcomings are intrinsically associated with
blood circulation. Patient-specific computational modeling proved
extremely beneficial for other cardiovascular issues (like the treatment of
aneurysms, left ventricular arrhythmia, or atrial fibrillation), but it has so far
not been used to study VV-ECMO. ECMOPT will exploit the potential of
Computational Fluid Dynamics (CFD) simulations on real geometries,
coupled with patient-specific data obtained from Kepler University Klinikum
to better understand the dynamics at play during VV-ECMO and devise a
shape optimization tool to maximize the efficiency of the reinsertion
cannula for oxygenated blood.
Status | Active |
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Effective start/end date | 01.10.2023 → 30.09.2025 |
Fields of science
- 101020 Technical mathematics
- 206001 Biomedical engineering
- 102009 Computer simulation
- 302004 Anaesthesiology
- 101 Mathematics
- 305901 Computer-aided diagnosis and therapy
- 302031 Intensive care medicine
- 101028 Mathematical modelling
- 101027 Dynamical systems
- 102003 Image processing
- 102023 Supercomputing
- 102001 Artificial intelligence
- 101004 Biomathematics
- 101014 Numerical mathematics
- 102035 Data science
- 101013 Mathematical logic
- 102019 Machine learning
- 202027 Mechatronics
- 101024 Probability theory
- 206003 Medical physics
JKU Focus areas
- Digital Transformation