Ultra-sensitive detection of carcinoembryonic antigen achieved by a dual – antibody sandwich – structured SERS immunosensor integrated with artificial intelligence

Carcinoembryonic antigen (CEA), a critical colorectal cancer biomarker, playing a pivotal role in the early diagnosis, therapeutic intervention and longitudinal monitoring of malignancies. Conventional detection methods such as ELISA exhibit inherent limitations including inadequate analytical sensitivity and prolonged assay duration. This study proposes the development of a SERS-based immunosensor integrated with artificial intelligence (AI) frameworks, aiming to achieve precise and quantitative detection of CEA. The antibody-conjugated gold nanoprobes (Au NPs/4-MBA/Ab 1 ) were immobilized onto the surface of another antibody-functionalized Ag/Cu/PSS substrate (Ab 2 /Ag/Cu/PSS) via specific biorecognition. This sandwich-structured immunosensor, engineered with blockage of non-specific binding sites, demonstrated enhanced capacity to capture CEA antigens through dual antibody recognition layers. The immunosensor enabled efficient amplification of SERS signals and exhibited excellent linearity within the CEA concentration range of 0.1–1000 ng/mL. Importantly, Support Vector Machines (SVM), Convolutional Neural Networks (CNN) and Transformer frameworks, were employed for hierarchical processing of CEA Raman data. The Transformer model demonstrated superior performance with a classification accuracy of 99.06 % and a low loss value of 0.1056. These results underscore the model's exceptional capability to discriminate CEA concentrations with high precision and robustness. The integration of AI-driven signal processing boosts analytical sensitivity of SERS-based immunosensors. This synergy between nanophotonic sensing and deep learning algorithms represents a transformative leap in early cancer detection via non-invasive biofluid analysis.