Advancing SERS Diagnostics in COVID-19 with Rapid, Accurate, and Label-Free Viral Load Monitoring in Clinical Specimens via SFNet Enhancement

Abstract

This study presents an integrated approach combining surface-enhanced Raman spectroscopy (SERS) with a specialized deep learning algorithm, SFNet, to offer a rapid, accurate, and label-free alternative for COVID-19 diagnosis and viral load quantification. The SiO2-coated silver nanorod arrays are employed as the SERS substrates, fabricated using a reliable and effective glancing angle deposition technique. A dataset of 4800 SERS spectra from 120 positive and 120 negative inactivated clinical human nasopharyngeal swabs are collected directly on the SERS substrates without any labels. A SFNet algorithm is tailored to adapt to the unique spectral features inherent to SERS data, achieving a test accuracy of 98.5% and a blind test accuracy of 99.04%. Moreover, an optimized SFNet algorithm unveils the capability of estimating SARS-CoV-2 viral loads, accurately predicting the cycle threshold values (Ct values) of the three vital gene fragments with a root mean square error (RMSE) of 1.627 (1.3 for blind test). The methodology is substantiated using actual clinical specimens and completed in <15 min, thereby strengthening its real-world point-of-care applicability. This rapid and precise yet label-free modality competes favorably with classical reverse-transcription real-time polymerase chain reaction (RT-PCR) and marks an advancement in SERS-based sensor algorithms.