Rapid Detection of SARS-CoV-2 RNA in Human Nasopharyngeal Specimens Using Surface-Enhanced Raman Spectroscopy and Deep Learning Algorithms.
Yanjun YangHao LiLes JonesJackelyn MurrayJames HaverstickHemant K NaikareYung-Yi C MosleyRalph A TrippBin AiYiping ZhaoPublished in: ACS sensors (2022)
A rapid and cost-effective method to detect the infection of SARS-CoV-2 is fundamental to mitigating the current COVID-19 pandemic. Herein, a surface-enhanced Raman spectroscopy (SERS) sensor with a deep learning algorithm has been developed for the rapid detection of SARS-CoV-2 RNA in human nasopharyngeal swab (HNS) specimens. The SERS sensor was prepared using a silver nanorod array (AgNR) substrate by assembling DNA probes to capture SARS-CoV-2 RNA. The SERS spectra of HNS specimens were collected after RNA hybridization, and the corresponding SERS peaks were identified. The RNA detection range was determined to be 10 3 -10 9 copies/mL in saline sodium citrate buffer. A recurrent neural network (RNN)-based deep learning model was developed to classify 40 positive and 120 negative specimens with an overall accuracy of 98.9%. For the blind test of 72 specimens, the RNN model gave a 97.2% accuracy prediction for positive specimens and a 100% accuracy for negative specimens. All the detections were performed in 25 min. These results suggest that the DNA-functionalized AgNR array SERS sensor combined with a deep learning algorithm could serve as a potential rapid point-of-care COVID-19 diagnostic platform.
Keyphrases
- raman spectroscopy
- sars cov
- deep learning
- nucleic acid
- gold nanoparticles
- respiratory syndrome coronavirus
- machine learning
- artificial intelligence
- convolutional neural network
- fine needle aspiration
- sensitive detection
- neural network
- label free
- single molecule
- loop mediated isothermal amplification
- high throughput
- ultrasound guided
- amino acid
- tandem mass spectrometry
- living cells