AI‐Assisted Customizable SERS‐Based Aptasensor for Label‐Free, Rapid, and Sensitive Detection of Tau: Comparative Analysis Using Random Forest and Convolutional Neural Networks

Early diagnosis of Alzheimer's disease (AD) is challenging due to the limitations of current biomarker detection methods. A customizable SERS‐based aptasensor platform is presented that combines aptamer‐functionalized gold nanoparticles (AuNPs) with machine learning (ML) algorithms for rapid and sensitive tau protein quantification, a key biomarker for AD. Through systematic evaluation of nanoprobes conjugated with four different tau‐specific aptamer sequences, two aptamer configurations, named AT and BT, are identified as optimal candidates, demonstrating enhancement factors (EFs) of 2.12 × 10 3 and 1.82 × 10 3 , respectively. This approach enables label‐free detection within 30 min and integrates Random Forest (RF) and Convolutional Neural Network (CNN) models for concentration prediction of unknown samples. The RF models achieve remarkable accuracy with R 2 values of 0.998 for AT and 0.9999 for BT configurations, while the CNN models demonstrate strong performance with R 2 values of 0.968 (AT) and 0.986 (BT). The platform achieves a detection limit of 100 pM, well within the clinically relevant ranges. This label‐free approach offers advantages in terms of rapid detection time, portability, and potential adaptability to other biomarkers. The integration of direct SERS sensing with ML algorithms for automated concentration prediction represents a promising advancement in biomarker analysis.