English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

The Pulmonary Function Test (PFT) is an widely utilized and rigorousclassification test for lung function evaluation, serving as a comprehensivetool for lung diagnosis. Meanwhile, Electrical Impedance Tomography (EIT) is arapidly advancing clinical technique that visualizes conductivity distributioninduced by ventilation. EIT provides additional spatial and temporalinformation on lung ventilation beyond traditional PFT. However, relying solelyon conventional isolated interpretations of PFT results and EIT imagesoverlooks the continuous dynamic aspects of lung ventilation. This study aimsto classify lung ventilation patterns by extracting spatial and temporalfeatures from the 3D EIT image series. The study uses a Variational Autoencodernetwork with a MultiRes block to compress the spatial distribution in a 3Dimage into a one-dimensional vector. These vectors are then concatenated tocreate a feature map for the exhibition of temporal features. A simpleconvolutional neural network is used for classification. Data collected from137 subjects were finally used for training. The model is validated by ten-foldand leave-one-out cross-validation first. The accuracy and sensitivity ofnormal ventilation mode are 0.95 and 1.00, and the f1-score is 0.94.Furthermore, we check the reliability and feasibility of the proposed pipelineby testing it on newly recruited nine subjects. Our results show that thepipeline correctly predicts the ventilation mode of 8 out of 9 subjects. Thestudy demonstrates the potential of using image series for lung ventilationmode classification, providing a feasible method for patient prescreening andpresenting an alternative form of PFT.

Spatio-Temporal Classification of Lung Ventilation Patterns using 3D EIT Images: A General Approach for Individualized Lung Function Evaluation