Predicting the risk of chronic kidney disease based on uric acid concentration in stones using biosensors integrated with a deep learning-based ANN system

• Published in: Talanta (Oxford) 2025-02, Vol.283, p.127077, Article 127077
• Main Authors: Chang, Yaw-Jen, Lin, Chu-Hao, Chien, You-Chiuan
• Format: Article
• Language: English
• Subjects: Artificial neural network; Biosensing Techniques - methods; Chronic kidney disease; Creatinine; Cyclic voltammetry; Deep Learning; Electrodes; Humans; Kidney Calculi - blood; Kidney Calculi - chemistry; Kidney Calculi - diagnosis; Male; Neural Networks, Computer; Renal Insufficiency, Chronic - blood; Renal Insufficiency, Chronic - diagnosis; Uric acid; Uric Acid - analysis; Uric Acid - blood
• ISSN: 0039-9140; 1873-3573; 1873-3573
• DOI: 10.1016/j.talanta.2024.127077

Elevated levels of uric acid (UA) in the body may not only lead to the formation of stones but also increase the risk of developing chronic kidney disease (CKD). This study presents a biosensor for detecting UA concentration in stones and a deep learning-based artificial neural network (ANN) system for analyzing CKD risk. The biosensor is a screen-printed electrode (SPE) chip, whose surface was modified using oxygen plasma, enabling the detection of UA concentration via cyclic voltammetry. Experimental results show a good linear relationship between UA concentration and anodic peak current within the range of 0.15–5 mM. The surface modification method for this biosensor is simple and cost-effective. The ANN system took age and creatinine values as inputs, utilizing the Chronic_Kidney_Disease dataset and supplementary data from literatures for training. After detecting the UA concentration in stones using the biosensor, the result was converted into serum uric acid concentration, allowing the estimation of creatinine level, which was then used by the ANN to assess the risk of developing CKD. This system can assist urologists in determining whether patients should seek consultation with nephrologists for early diagnosis and treatment. [Display omitted] •A biosensor for detecting uric acid concentration in stones.•A deep learning-based ANN system for analyzing the risk of chronic kidney disease.•To assist urologists in determining whether patients should consult nephrologists.