Detection of SARS-CoV-2 in patient specimens by surface enhanced Raman spectroscopy and deep learning

Yanjun Yang; Hao Li; Dan Luo; Jiaheng Cui; Amit Kumar; Leslie Jones; Jackelyn Crabtree; Hemant Naikare; Yung-Yi C. Mosley; Teddy Spikes; Sebastian Hülck; Xianyan Chen; Ralph A. Tripp; Bin Ai; Yiping Zhao

doi:10.1117/12.3021632

20 June 2024 Detection of SARS-CoV-2 in patient specimens by surface enhanced Raman spectroscopy and deep learning

Yanjun Yang, Hao Li, Dan Luo, Jiaheng Cui, Amit Kumar, Leslie Jones, Jackelyn Crabtree, Hemant Naikare, Yung-Yi C. Mosley, Teddy Spikes, Sebastian Hülck, Xianyan Chen, Ralph A. Tripp, Bin Ai, Yiping Zhao

Author Affiliations +

Proceedings Volume 12999, Optical Sensing and Detection VIII; 129991I (2024) https://doi.org/10.1117/12.3021632
Event: SPIE Photonics Europe, 2024, Strasbourg, France

Abstract

Diagnosis of SARS-CoV-2 infection allows for disease intervention, control, and management for COVID-19. The realtime reverse transcriptase-polymerase chain reaction (RT-PCR) is considered the gold standard used to detect the virus. Due to the high testing volumes, the process has a long turnaround time, i.e., 2-3 days. It also requires expensive equipment and involves highly trained staff. Other fast diagnostic methods, such as lateral flow assay based on antibody detection, have limitations such as lower specificity and sensitivity. Thus, there is a critical need for a rapid and low-cost point-ofcare (POC) diagnostic method to accurately diagnose SARS-CoV-2 infections in patients. In this study, three rapid, portable, and cost-effective methods to detect SARS-CoV-2 in human nasopharyngeal swab specimens are developed using surface enhanced Raman spectroscopy (SERS) and deep learning: RNA hybridization, ACE-2 capture, and direct detection. Combining the SERS spectra with a deep learning algorithm, all methods can achieve > 99% accuracy to classify the positive and negative specimens and the test-to-answer time is within 30 min. The RNA hybridization method can achieve a limit of detection of 1000 copies/ml, and the ACE-2 method is capable of differentiating between different variants of SARS-CoV-2 viruses. The direct detection method can additionally quantitatively predict the cycle threshold (Ct) value of RT-PCR tests for positive specimens, demonstrating a diagnostic accuracy of 99.04% in blind tests of 104 specimens. These results indicate that SERS combined with deep learning could be a potential rapid POC COVID-19 diagnostic platform.

Conference Presentation

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Yanjun Yang, Hao Li, Dan Luo, Jiaheng Cui, Amit Kumar, Leslie Jones, Jackelyn Crabtree, Hemant Naikare, Yung-Yi C. Mosley, Teddy Spikes, Sebastian Hülck, Xianyan Chen, Ralph A. Tripp, Bin Ai, and Yiping Zhao "Detection of SARS-CoV-2 in patient specimens by surface enhanced Raman spectroscopy and deep learning", Proc. SPIE 12999, Optical Sensing and Detection VIII, 129991I (20 June 2024); https://doi.org/10.1117/12.3021632

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