Portable electrical impedance tomography (EIT) system stages non-alcoholic fatty liver disease for potential screening and monitoring at home.

This study demonstrates the feasibility of predicting NAFLD using multi-spectral electrical impedance tomography (EIT), group source separation, constant reference EIT and anthropometric measures. Vibration-controlled Transient Elastography (VCTE) Controlled Attenuated Parameter (CAP; n = 121) and magnetic resonance imaging-proton density fat fraction (MRI-PDFF; n = 34) achieved a sensitivity of 70.9% and specificity of 73.

Deep learning based reconstruction enables high-resolution electrical impedance tomography for lung function assessment.

Recently, deep learning based methods have shown potential as alternative approaches for lung time difference electrical impedance tomography (tdEIT) reconstruction other than traditional regularized least square methods, that have inherent severe ill-posedness and low spatial resolution posing challenges for further interpretation. However, the validation of deep learning reconstruction quality is mainly focused on simulated data rather than in vivo human chest data, and on image quality rather than clinical indicator accuracy. In this study, a variational autoencoder is trained on high-resolution human chest simulations, and inference results on an EIT dataset collected from 22 healthy subjects performing various breathing paradigms are benchmarked with simultaneous spirometry measurements.

Global and regional lung function assessment using portable electrical impedance tomography (EIT) system: clinical study.

Recent development of affordable, portable and self-administrable electrical impedance tomography (EIT) system demonstrated the feasibility of using standalone EIT and subject's anthropometrics to predict the gold standard spirometry indicators for lung-function assessment. Compared to spirometry, the system showed the advantage of providing spatial mapping of the spirometry indicators. Nevertheless, the previous study was limited to healthy subjects.

Unmixing multi-spectral electrical impedance tomography (EIT) predicts clinical-standard controlled attenuation parameter (CAP) for nonalcoholic fatty liver disease classification: a feasibility study.

Here, we tested the feasibility of predicting CAP with multi-spectral EIT. Conductivity and CAP were acquired from nonalcoholic fatty liver disease patients using a portable EIT system and vibration-controlled transient elastography (VCTE). We then used frequency-difference conductivity and waist-over-height as prediction features to estimate CAP and found an adj.