Rapid and Differential Diagnosis of Sepsis Stages Using an Advanced 3D Plasmonic Bimetallic Alloy Nanoarchitecture‐Based SERS Biosensor Combined with Machine Learning for Multiple Analyte Identification

Abstract Rapid and accurate differential diagnosis of infections, sepsis, and septic shock is essential for preventing unnecessary antibiotic overuse and improving the chance of patient survival. To address this, a 3D gold nanogranule decorated gold‐silver alloy nanopillar (AuNG@Au‐AgNP) based surface‐enhanced Raman scattering (SERS) biosensor is developed, capable of quantitatively profiling immune‐related soluble proteins (interleukin three receptor, alpha chain: CD123, programmed cell death ligand 1: PD‐L1, human leukocyte antigen–DR isotype: HLA‐DR, and chitotriosidase: ChiT) in serum samples. The 3D bimetallic nanoarchitecture, fabricated using anodized aluminum oxide (AAO), features a uniform structure with densely packed nanogaps on the heads of Au‐Ag alloy nanopillars, enabling fast, simple, and replicable production. The proposed biosensor achieves accurate results even with low detection limits (4–6 fM) and high signal consistency (relative standard deviation (RSD) = 1.79%) within a one‐step multi‐analytes identification chip with a directly loadable chamber. To enhance the diagnostic performance, a support vector machine (SVM) based machine learning algorithm is utilized, achieving 95.0% accuracy and 95.8% precision in classifying healthy controls, infections with and without sepsis, and septic shock. This advanced 3D plasmonic bimetallic alloy nanoarchitecture‐based SERS biosensor demonstrates clinical usefulness for sepsis diagnosis and severity assessment, providing timely and personalized treatment.