  Electrical impedance tomography (EIT) is a noninvasive imaging modality that
allows a continuous assessment of changes in regional bioimpedance of different
organs. One of its most common biomedical applications is monitoring regional
ventilation distribution in critically ill patients treated in intensive care
units. In this work, we put forward a proof-of-principle study that
demonstrates how one can reconstruct synchronously measured respiratory or
circulatory parameters from the EIT image sequence using a deep learning model
trained in an end-to-end fashion. We demonstrate that one can accurately infer
absolute volume, absolute flow, normalized airway pressure and within certain
limitations even the normalized arterial blood pressure from the EIT signal
alone, in a way that generalizes to unseen patients without prior calibration.
As an outlook with direct clinical relevance, we furthermore demonstrate the
feasibility of reconstructing the absolute transpulmonary pressure from a
combination of EIT and absolute airway pressure, as a way to potentially
replace the invasive measurement of esophageal pressure. With these results, we
hope to stimulate further studies building on the framework put forward in this
work.
