DSFNet: Dual-source and spatiotemporal-feature fusion network for bedside diagnosis of lung injuries with electrical impedance tomography

Electrical Impedance Tomography (EIT) is a promising tool for non-invasive and real-time lung monitoring, but the data heterogeneity and low spatial resolution limit its ability to diagnose lung injuries. To address these challenges, we propose DSFNet, a dual-source and spatiotemporal-feature fusion network that integrates EIT spatiotemporal boundary voltages and ventilation images to classify four lung conditions, including healthy (HE), pneumothorax (PN), pleural effusion (PE), and pneumonia (PM). The temporal dynamics modeling (TDM) module and multi-head self-attention (MHSA) module are designed to improve the temporal feature extraction and representation of DSFNet. We construct a novel EIT simulation dataset describing pathological respiratory patterns and introduce a hybrid data learning strategy that combines simulation data (SD) and experimental data (ED) to address the small sample problem and improve the accuracy of model classification. The DSFNet trained with the SD + 25 % ED pattern achieved an accuracy of 97.78 % and 96.55 % on the dynamic phantom dataset and the clinical human dataset, respectively, demonstrating its excellent performance and robustness. The SHAP analysis further revealed the feature contributions of the input data. This study provides an effective approach for bedside lung injury diagnosis based on multi-source EIT data.