Toward Prostate Cancer Early Warning with a Self‐Powered Wearable Biosensing Platform Integrated with Machine Learning

ABSTRACT Current prostate cancer detection methods remain limited in non‐invasiveness and specificity, prompting interest in urinary biomarkers such as sarcosine. Here, we report a urine‐powered wearable platform for non‐invasive sarcosine detection as a proof‐of‐concept for decentralized early warning. Distinct from prior urine‐powered sensors, this platform establishes a hybrid bio‐electrochemical energy paradigm in which urine simultaneously functions as the analytical sample and an intrinsic energy reservoir. Specifically, sarcosine serves as the biofuel to drive an enzymatic fuel cell, while endogenous Zn 2 + ions in urine power a complementary aqueous battery component, enabling synergistic signal amplification and stable operation. This synergistic design enables strong signal amplification and operational stability, achieving a detection limit as low as 0.85 p M . Dual electrochemical and colorimetric readouts allow flexible result interpretation. A hybrid classification‐regression model is implemented to identify high‐concentration samples using a learned threshold, followed by range‐specific regression to reduce dynamic‐range interference and improve quantitative accuracy. The system is further integrated into a smart diaper format with wireless connectivity for on‐demand smartphone‐based analysis. Preliminary validation using human urine samples shows satisfactory analytical performance, with spike‐and‐recovery values ranging from 83.68% to 125.88%. This work provides a proof‐of‐concept demonstration of a sarcosine‐centered, self‐powered sensing strategy for non‐invasive biomarker detection.