Research on a rapid and accurate diagnosis platform for liver fibrosis based on machine learning-assisted SERS...

Rapid and accurate diagnosis of liver fibrosis (LF) is essential for timely clinical intervention, yet traditional diagnostic methods are frequently constrained by low sensitivity and limited accuracy, representing a major obstacle to early liver disease screening. In this work, we present a label-free, non-invasive detection strategy that integrates surface-enhanced Raman spectroscopy (SERS) technology with an optimized machine learning (ML), principal component analysis (PCA)-K-means-+. In detail, Au colony-like nanoarray substrate (AuCLNAs) was fabricated as a SERS active platform to acquire high-quality SERS spectra of serum from LF mice. The PCA-K-means-+ algorithm was then employed to construct and train a classification model. The results demonstrate that the substrate exhibits excellent uniformity, stability, and SERS enhancement. The PCA-K-means-+ model effectively extracted key spectral features, achieving robust classification with an accuracy of 99.0%, a sensitivity of 98.8%, a specificity of 100%, and an area under the curve (AUC) of 0.992. These findings highlight the significant potential of this SERS technology combined with the PCA-K-means-+ model in identifying subtle spectral variations at different stages, offering a promising and reliable tool for the precise clinical diagnosis of LF.