The Barkhausen Institut has developed working prototypes that ensure secure and trustworthy radio communication between patient monitoring devices.

To ensure that the trained computer model is always aware of the current treatment status during an operation and can generate treatment suggestions, the patient monitor, including pulse and blood pressure measurement, and the ventilator are wirelessly connected to the PC.

In the IntelliLung project of the Else Kröner Fresenius Center for Digital Health, a system is being developed in collaboration with the Pulmonary Engineering Group of the TU Dresden, the InfAI and the University Hospital Carl Gustav Carus, which uses machine learning to support anesthesiologists in finding the best possible treatment for each individual patient.

ICU patients with acute respiratory failure usually require pulmonary function support achieved by mechanical ventilation and, in difficult cases, by gas exchange outside the body. Although mechanical ventilation is a life-saving therapy, it bears the risk to complicate lung failure and compromise blood flow.

Currently, there are several strategies to protect the lungs from ventilator damage. However, these can differ significantly in terms of the mechanical energy transmitted and its distribution across the lung tissue. Parameters on the ventilator and on the lung support device can be set automatically. Nevertheless, expert handling may differ from these settings depending on the clinical characteristics of individual patients. Artificial intelligence can be used to learn from these deviations, as well as from the patient's condition, to improve the combination of settings and achieve lung support with reduced risk of damage.

The Barkhausen Institut is working on the sub-project for simple, secure and reliable wireless networking of the devices involved.