Abstract
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.
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