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. Here, we recruited (N=88): 47 lung disease patients and 41 healthy controls to perform simultaneous EIT and spirometry measurements to validate the capabilities of the system. Lung disease patients include 13 interstitial lung disease (ILD), 10 asthma, 8 chronic obstructive pulmonary disease (COPD), 8 bronchiectasis, and 8 with other diseases including left pneumonectomy, lung cancer, lung tumor, lymphangioleiomyomatosis, motor neuron disease, heart failure and bronchiolitis obliterans syndrome. The results showed significant correlation of the predicted global spirometry indicators (p<0.0001) and significant distinguishability between most disease groups and healthy subjects demonstrating the capability of the EIT system in diagnostic screening. Furthermore, the regional mapping of the spirometry indicators is evaluated and shown to be distinct for each disease group, providing an additional dimension for medical professionals to diagnose and monitor lung disease patients.Clinical Relevance- This establishes the significance of EIT-based global and regional indicators for assessing lung function on lung disease patients.

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. The deep learning reconstructed global conductivity is significantly correlated with measured volume-time curves with correlation > 0.9. EIT lung function indicators from the reconstruction are also highly correlated with standard spirometry indicators with correlation > 0.75.Clinical Relevance- Our deep learning reconstruction method of lung tdEIT can predict lung volume and spirometry indicators while generating high-resolution EIT images, revealing potential of being a competitive approach in clinical settings.

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 73.8% with our CAP predicting model and sensitivity of 77.8% and specificity of 80.0% with our MRI-PDFF predicting model. In summary, a portable EIT can be a cost-effective and self-administrable alternative for widespread home-based and community-based diagnostic screening and treatment monitoring of NAFLD.Clinical Relevance- Portable multi-spectral EIT system has the sensitivity and specificity to potentially unlock biomedical imaging in telemedicine for home-based and community-based screening, staging and monitoring for NAFLD.

Electric impedance tomography enables portable and non-invasive approach to screen and monitor chronic kidney disease.

Chronic kidney disease (CKD) is an escalating global health concern, and non-invasive means for early CKD detection is eagerly awaited. Here, we explore the potential of using home-based frequency-difference electrical impedance tomography (fdEIT) to evaluate CKD based on bio-conductivity characteristics. We performed bio-conductivity measurement in vivo paired with standard estimated glomerular filtration rate (eGFR) measurements on a N=126 CKD patients by EIT and traditional blood and urine tests, respectively. We developed an EIT processing pipeline that extracts the kidney regions from EIT images. We further developed a regression model and a CKD classification scheme. Our results showed a significant correlation between EIT-features and eGFR, and the classification scheme shows sensitivity and specificity of 76.2% and 74.6% respectively considering stages 1 and 2 CKD versus stages 3, 4 and 5 CKD. These results suggest the feasibility of EIT to be used as a portable, self-administrated and home-based approach for CKD early diagnostic screening and longitudinal monitoring.Clinical Relevance-The results presented here demonstrates a cost-effective, home-based and self-administrative screening process on chronic kidney disease patients, thereby enhancing the quality and area of possible application of telemedicine. By achieving this, the process presented here can relieve the burden of public health system.

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. R2 of 0.92. We further developed a novel prediction method by incorporating EIT spectral unmixing reconstruction and demonstrated an improvement in CAP estimation. Last, we optimized the EIT acquisition process by minimizing the total variance of the CAP estimator. Clinical Relevance: EIT can estimate clinical-standard liver disease classification. This portable EIT system is potentially cost-effective and self-administrable with short acquisition time (3 mins), while VCTE are costly and usually requires a trained personnel to operate with longer acquisition time (5-10 mins).