Early thrombus detection in ECMO with optimized impedance measurements: A simulative study

Abstract

Extracorporeal oxygenation supports patients with severe cardiac or respiratory failure, with the oxygenator providing critical gas exchange. Thrombus formation in the oxygenator can impair efficiency and increase risks such as hemolysis and embolism, but existing detection methods are limited in accuracy and timeliness. This study introduces a computational bioimpedance approach for early thrombus detection that integrates advanced modeling and machine learning techniques while preserving the oxygenator’s functionality.

Keywords

• thrombus detection
• finite element method
• machine learning
• sensitivity analysis