AI-Empowered and a Bio-/Nanoenzyme-Hybrid Multisensors Array toward Precision Diagnosis of Kidney Diseases

Chronic kidney disease (CKD) risk assessment is shifting from centralized instrument-heavy testing to personalized point-of-care evaluation enabled by portable platforms. However, integration of multibiomarker analysis and the deep learning algorithm to improve detection accuracy in CKD is still a persistent goal. Herein, this work has developed an artificial intelligence (AI)-empowered and bio-/nanoenzyme-hybrid multisensors array (AI-BMA) for multidimensional precise diagnosis of early stage CKD. The laser-induced electrochemical sensors array is functionalized by creatinine deaminase (CDI), urate oxidase (UOx), and polyaniline for the differential detection of urinary creatinine (Cr), uric acid (UA), and pH. The integrated analysis of multimodal/multimarker electrochemical characteristic spectra and one-dimensional convolutional neural network (1D-CNN) along with multilayer perceptron (MLP) establishes an end-to-end workflow from electrochemical signal acquisition to individualized CKD risk assessment. The proposed bio-/nanoenzyme-hybrid multiplexed sensors strategy demonstrates well-defined analytical performance, covering detection ranges of 3-15 mM creatinine with limit of detection of 300.50 μM, 0.1-1.0 mM uric acid with LOD value of 19.17 μM, and 3.0-9.0 pH. By employing the self-developed 1D-CNN and MLP model for multimarker joint prediction, the average prediction accuracy of CKD biomarkers reaches 98.67%. This overcomes limitations of high-dimensional electrochemical signal feature extraction and multi-index joint prediction. The robust AI-BMA platform can automatically convert complex electrochemical detection data of urinary metabolites into understandable risk stratification results. This provides an alternative solution for the early screening of kidney injury, which is expected to assist patients/clinicians in identifying CKD's risk assessment in the home-care scenario and limited resources.