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

Abstract

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.

Keywords

• deep learning
• SARS‐CoV‐2
• silver nanorod array
• surface‐enhanced Raman scattering (SERS)
• viral load