Spatiotemporal Residual Recurrent Neural Network for Lung Function Evaluation Using Electrical Impedance Tomography

The growing prevalence of pulmonary diseases underscores the urgent need for early diagnosis to reduce mortality rates. Traditional pulmonary function tests (PFTs) often require active patient participation, making them difficult to administer in certain populations. This study presents a novel method for lung function evaluation during breathing using spatiotemporal residual recurrent neural networks (STResRNNs) based on electrical impedance tomography (EIT). Our method integrates a residual network (ResNet) with an hybrid attention module (HAM) and a bidirectional long short-term memory (BiLSTM) structure to effectively extract spatiotemporal features from EIT image sequences. This allows for accurate classification of lung ventilation function, distinguishing between normal, obstructive, and injured conditions without the need for forceful breathing maneuvers. In a clinical trial with 165 participants, the STResRNN achieved an overall accuracy of 97.4%. The ${F}1$ scores for Health, chronic obstructive pulmonary disease (COPD), and injury were 98.4%, 97.5%, and 96.3%, respectively. These results demonstrate the robustness of the model in identifying various lung conditions. Further parameter optimization and ablation studies validated the effectiveness of the network’s components. This research highlights the potential of EIT-based respiratory data for noninvasive and effortless lung function evaluation, paving the way for early pulmonary disease screening and expanding the clinical applications of EIT.