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Machine Learning Enabled Portable Electrical Impedance Tomography System for Community Level Chronic Kidney Disease

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    Summary
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    A novel portable electrical impedance tomography (EIT) system with machine learning offers non-invasive chronic kidney disease (CKD) screening and eGFR estimation. This technology promises accessible, low-cost CKD detection for community and home settings.

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    Area of Science:

    • Biomedical Engineering
    • Medical Diagnostics
    • Machine Learning in Healthcare

    Background:

    • Chronic kidney disease (CKD) affects 850 million globally, presenting significant healthcare challenges due to late detection and costly treatments.
    • Current diagnostic methods (serum creatinine, urinary albumin) are invasive, expensive, and lab-dependent, limiting remote monitoring.
    • There is an unmet need for accessible, non-invasive CKD screening and eGFR estimation, particularly for community and home-based settings.

    Purpose of the Study:

    • To develop and validate a novel, portable, non-invasive approach for CKD screening and eGFR estimation using electrical impedance tomography (EIT) and machine learning.
    • To assess the feasibility of integrating patient-specific anthropometric data with EIT signals for enhanced diagnostic accuracy.
    • To demonstrate the potential of this system for point-of-care CKD detection and remote monitoring.

    Main Methods:

    • A portable EIT system was used to assess 138 subjects (healthy volunteers and CKD patients).
    • Renal EIT signals were processed using principal component analysis (PCA) for feature extraction.
    • A two-stage machine learning model (SVM for classification, XGBoost for eGFR prediction) was developed, incorporating anthropometric parameters.

    Main Results:

    • The EIT-based system achieved 93% accuracy in classifying healthy individuals from CKD patients (Stages 2-4), with 100% sensitivity and 94% specificity (AUC-ROC: 0.97).
    • The eGFR prediction model achieved an R² value of 0.57 (p < 0.001), demonstrating significant differences across CKD stages.
    • The proposed approach outperformed anthropometric-based models in both classification and regression tasks.

    Conclusions:

    • A low-cost, portable EIT system combined with machine learning is feasible for point-of-care CKD detection and eGFR estimation.
    • This non-invasive system has the potential to enable widespread CKD screening in community and telemedicine settings, improving early detection and management.
    • The technology addresses the urgent need for accessible, self-administrable CKD monitoring, potentially reducing healthcare costs.