Machine Learning-Enhanced Flexible IL-6 Sensor for Rapid Threshold Detection

Cytokine detection—particularly interleukin-6 (IL-6)—is of growing interest due to its role in chronic inflammation and acute conditions like sepsis, where elevated concentrations above physiological levels require urgent clinical attention. Given the strong correlation between sweat and blood IL-6 levels, wearable sweat-based detection offers a promising non-invasive monitoring approach. Electrochemical sensors are especially well-suited for such applications due to their high sensitivity and accuracy. Among these, electrochemical impedance spectroscopy (EIS) provides excellent sensitivity but is challenging to implement in embedded, low-power systems. Conversely, cyclic voltammetry (CV) is more compatible with low-power, wearable devices but generally lacks the sensitivity needed for ultra-low concentration detection. To advance CV-based IL-6 sensing, this study establishes the core electrochemical and analytical performance of a novel CV-based sensor under controlled laboratory conditions. We developed a flexible electrochemical biosensor using a screen-printed carbon three-electrode system functionalized with gold nanoparticles and IL-6-specific aptamers. Electrochemical characterization was conducted using both EIS and CV with a benchtop potentiostat in 1x phosphate-buffered saline (PBS). To enhance threshold-based classification, machine learning (ML) was integrated with CV data analysis. Using a k-Nearest Neighbors (KNN) algorithm, we classified CV datasets with 96.7% accuracy, distinguishing IL-6 concentrations in 1x PBS as physiological (<20 pg/mL) or pathological (>20 pg/mL). These findings demonstrate the potential of combining electrochemical sensing with ML for sensitive, threshold-based detection of ultra-low analyte concentrations, supporting future translation toward wearable health monitoring platforms.