Author: Yin, Gang; Li, Lintao; Lu, Shun; Yin, Yu; Su, Yuanzhang; Zeng, Yilan; Luo, Mei; Ma, Maohua; Zhou, Hongyan; Orlandini, Lucia; Yao, Dezhong; Liu, Gang; Lang, Jinyi
Title: An efficient primary screening of COVIDâ€19 by serum Raman spectroscopy Cord-id: i9ejwqrq Document date: 2021_2_19
ID: i9ejwqrq
Snippet: The outbreak of COVIDâ€19 coronavirus disease around the end of 2019 has become a pandemic. The preferred method for COVIDâ€19 detection is the realâ€time polymerase chain reaction (RTâ€PCR)â€based technique; however, it also has certain limitations, such as sampleâ€dependent procedures with a relatively high false negative ratio. We propose a safe and efficient method for screening COVIDâ€19 based on Raman spectroscopy. A total of 177 serum samples are collected from 63 confirmed COVIDâ€
Document: The outbreak of COVIDâ€19 coronavirus disease around the end of 2019 has become a pandemic. The preferred method for COVIDâ€19 detection is the realâ€time polymerase chain reaction (RTâ€PCR)â€based technique; however, it also has certain limitations, such as sampleâ€dependent procedures with a relatively high false negative ratio. We propose a safe and efficient method for screening COVIDâ€19 based on Raman spectroscopy. A total of 177 serum samples are collected from 63 confirmed COVIDâ€19 patients, 59 suspected cases, and 55 healthy individuals as a control group. Raman spectroscopy is adopted to analyze these samples, and a machine learning supportâ€vector machine (SVM) method is applied to the spectrum dataset to build a diagnostic algorithm. Furthermore, 20 independent individuals, including 5 asymptomatic COVIDâ€19 patients and 5 symptomatic COVIDâ€19 patients, 5 suspected patients, and 5 healthy patients, were sampled for external validation. In these three groups—confirmed COVIDâ€19, suspected, and healthy individuals—the distribution of statistically significant points of difference showed highly consistency for intergroups after repeated sampling processes. The classification accuracy between the COVIDâ€19 cases and the suspected cases is 0.87 (95% confidence interval [CI]: 0.85–0.88), and the accuracy between the COVIDâ€19 and the healthy controls is 0.90 (95% CI: 0.89–0.91), while the accuracy between the suspected cases and the healthy control group is 0.68 (95% CI: 0.67–0.73). For the independent test dataset, we apply the obtained SVM model to the classification of the independent test dataset to have all the results correctly classified. Our model showed that the serumâ€level classification results were all correct for independent test dataset. Our results suggest that Raman spectroscopy could be a safe and efficient technique for COVIDâ€19 screening.
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