Machine learning‐assisted serum SERS strategy for rapid and non‐invasive screening of early cystic echinococcosis

Abstract

Early and accurate diagnosis of cystic echinococcosis (CE) with existing technologies is still challenging. Herein, we proposed a novel strategy based on the combination of label‐free serum surface‐enhanced Raman scattering (SERS) spectroscopy and machine learning for rapid and non‐invasive diagnosis of early‐stage CE. Specifically, by establishing early‐ and middle‐stage mouse models, the corresponding CE‐infected and normal control serum samples were collected, and silver nanoparticles (AgNPs) were utilized as the substrate to obtain SERS spectra. The early‐ and middle‐stage discriminant models were developed using a support vector machine, with diagnostic accuracies of 91.7% and 95.7%, respectively. Furthermore, by analyzing the serum SERS spectra, some biomarkers that may be related to early CE were found, including purine metabolites and protein‐related amide bands, which was consistent with other biochemical studies. Thus, our findings indicate that label‐free serum SERS analysis is a potential early‐stage CE detection method that is promising for clinical translation.

Recent Trends in Surface-Enhanced Raman Spectroscopy-Based Biosensors: Label-Free Early Disease Diagnosis

“…A support vector machine (SVM) model, an effective classification algorithm, was utilized for disease diagnosis such as AD, , cancer, ,− and other diseases ,, based on SERS spectra. Andrea et al used the t -distributed stochastic neighbor embedding algorithm to visualize the SERS spectra of the end products of the 2D seed amplification assay .…”