Sensor Arrays for Urinary Tract Infection Diagnosis

Urinary tract infections (UTIs) are highly prevalent and require rapid bacteria identification to guide timely and precise antibiotic therapy. Although numerous analytical technologies have been developed for UTI‐related bacterial detection in preclinical simulations, their clinical diagnosis applications remain extremely limited. The major bottlenecks arise from the strong biochemical interference of real urine samples and the stringent sensitivity thresholds needed to detect clinically relevant bacterial loads. Sensor array technology integrating cross‐reactive sensing elements with machine‐learning‐based pattern recognition offers a promising route to overcome these challenges by enabling rapid, multiplex, and culture‐free bacteria identification. This review systematically summarizes recent advances in sensor array platforms for UTI diagnosis, from material design to detection mechanisms, with particular emphasis on strategies targeting clinical sample interference and sensitivity enhancement. We also outline a roadmap for translating these sensor arrays into practical, rapid, and reliable tools for point‐of‐care UTI diagnosis.