Deep learning-based estimation of lung collapse in electrical impedance tomography: a simulation and phantom study

Objective.In mechanically ventilated patients, inhomogeneity of air volume distribution in the lungs can lead to lung collapse and overdistention, increasing the risk of ventilator-induced lung injury. This study aims to estimate the degree of lung collapse (DoLC) from electrical impedance tomography (EIT) images without relying on lung segmentation.Approach.Traditional DoLC assessment based on the global inhomogeneity index is limited by lung segmentation. To address this limitation, a deep learning framework is proposed to directly estimate DoLC from EIT images. The model was trained on synthetic datasets simulating various lung conditions.Main results.The proposed method achieved high accuracy, with errors within 0%-5% in numerical and phantom tests across heterogeneous simulated lung conditions.Significance.The proposed framework enables segmentation-free estimation of DoLC from EIT images.