Model-based decision making by linking mathematical lung models with electrical impedance tomography
Modern medicine is inconceivable without technical support for management, diagnosis and therapy, but it is becoming increasingly complicated, expensive and error-prone. The automation of devices for individually adapted optimal therapy promises qualitative improvements and a reduction in costs, e.g. through shorter treatment times. All forms of knowledge about physiological processes, empirical findings and the embedding of medical procedures in clinical processes must be taken into account.
In recent years, the effects of model-based individualization on the quality of patient therapy have already been demonstrated. Building on this, the project is now combining imaging procedures, in particular electrical impedance tomography (EIT), with model systems to create intelligent advisors for the first time. This is an important step towards automated ventilation therapy. MOVE pursues two goals:
- how can model knowledge be used to improve the reconstruction of EIT images and
- how can EIT imaging be used to develop better, identifiable models for decision support at the bedside.
The combined expertise of the project partners will allow the doctoral students to successively find scientific solutions to the problems and present them as innovations to industrial partners for exploitation.
