Personalized protective ventilation of intensive care patients using a digital twin

Purpose and goal

The goal of this project is to provide intensive care clinicians with tools to ensure that mechanical ventilation does not cause harm to the patient’s lungs and diaphragm by developing a physiological model of the respiratory system that can be tuned to the individual patient, a digital twin. The system will be developed using novel AI-techniques in machine learning using data from a Getinge proprietary solution to measure the electrical activation of the diaphragm.

Expected effects and result

Mechanical ventilation is provided to more than 20 million patients worldwide every year. To ensure that the therapy is safe and effective there is a need to accurately monitor diaphragm and lung distending pressures in the individual patient. In this project we’ll use a digital twin to provide the means for personalized care. A new hybrid physiological and machine-learning model will be developed, for accurate estimation of clinically relevant pressures, and evaluated against patient data.

Planned approach and implementation

The project has two partners, one industrial, Getinge and one academic, KTH. Getinge provides the project with clinical knowledge, patient data, performs pre-processing and sets up the physiological lung model. KTH is responsible for machine-learning model construction, training and validation. KTH will also share knowledge, competence and understanding of machine learning to Getinge.