AI-boosted CRISPR-Cas13a and total internal reflection fluorescence microscopy system for SARS-CoV-2 detection
Integrating artificial intelligence with SARS-CoV-2 diagnostics can help in the timely execution of pandemic control and monitoring plans. To improve the efficiency of the diagnostic process, this study aims to classify fluorescent images via traditional machine learning and deep learning-based tran...
| Main Authors: | , , , , , , , , , , , , , , , , , , |
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2022-11-01
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| Series: | Frontiers in Sensors |
| Subjects: | |
| Online Access: | https://www.frontiersin.org/articles/10.3389/fsens.2022.1015223/full |
| _version_ | 1797227510645129216 |
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| author | Likun Zhang Likun Zhang Zhengyang Lei Zhengyang Lei Chufan Xiao Chufan Xiao Zhicheng Du Zhicheng Du Chenyao Jiang Chenyao Jiang Xi Yuan Xi Yuan Qiuyue Hu Qiuyue Hu Shiyao Zhai Shiyao Zhai Lulu Xu Lulu Xu Changyue Liu Changyue Liu Xiaoyun Zhong Xiaoyun Zhong Haifei Guan Haifei Guan Muhammad Hassan Muhammad Hassan Ijaz Gul Ijaz Gul Vijay Pandey Vijay Pandey Xinhui Xing Xinhui Xing Can Yang Zhang Can Yang Zhang Qian He Qian He Peiwu Qin Peiwu Qin |
| author_facet | Likun Zhang Likun Zhang Zhengyang Lei Zhengyang Lei Chufan Xiao Chufan Xiao Zhicheng Du Zhicheng Du Chenyao Jiang Chenyao Jiang Xi Yuan Xi Yuan Qiuyue Hu Qiuyue Hu Shiyao Zhai Shiyao Zhai Lulu Xu Lulu Xu Changyue Liu Changyue Liu Xiaoyun Zhong Xiaoyun Zhong Haifei Guan Haifei Guan Muhammad Hassan Muhammad Hassan Ijaz Gul Ijaz Gul Vijay Pandey Vijay Pandey Xinhui Xing Xinhui Xing Can Yang Zhang Can Yang Zhang Qian He Qian He Peiwu Qin Peiwu Qin |
| author_sort | Likun Zhang |
| collection | DOAJ |
| description | Integrating artificial intelligence with SARS-CoV-2 diagnostics can help in the timely execution of pandemic control and monitoring plans. To improve the efficiency of the diagnostic process, this study aims to classify fluorescent images via traditional machine learning and deep learning-based transfer learning. A previous study reported a CRISPR-Cas13a system combined with total internal reflection fluorescence microscopy (TIRFM) to detect the existence and concentrations of SARS-CoV-2 by fluorescent images. However, the lack of professional software and excessive manual labor hinder the practicability of the system. Here, we construct a fluorescent image dataset and develop an AI-boosted CRISPR-Cas13a and total internal reflection fluorescence microscopy system for the rapid diagnosis of SARS-CoV-2. Our study proposes Fluorescent Images Classification Transfer learning based on DenseNet-121 (FICTransDense), an approach that uses TIRF images (before and after sample introduction, respectively) for preprocessing, including outlier exclusion and setting and division preprocessing (i.e., SDP). Classification results indicate that the FICTransDense and Decision Tree algorithms outperform other approaches on the SDP dataset. Most of the algorithms benefit from the proposed SDP technique in terms of Accuracy, Recall, F1 Score, and Precision. The use of AI-boosted CRISPR-Cas13a and TIRFM systems facilitates rapid monitoring and diagnosis of SARS-CoV-2. |
| first_indexed | 2024-04-24T14:41:57Z |
| format | Article |
| id | doaj.art-b416a8a1b78b41fc9ef178ae88b6db2f |
| institution | Directory Open Access Journal |
| issn | 2673-5067 |
| language | English |
| last_indexed | 2024-04-24T14:41:57Z |
| publishDate | 2022-11-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Sensors |
| spelling | doaj.art-b416a8a1b78b41fc9ef178ae88b6db2f2024-04-02T21:29:39ZengFrontiers Media S.A.Frontiers in Sensors2673-50672022-11-01310.3389/fsens.2022.10152231015223AI-boosted CRISPR-Cas13a and total internal reflection fluorescence microscopy system for SARS-CoV-2 detectionLikun Zhang0Likun Zhang1Zhengyang Lei2Zhengyang Lei3Chufan Xiao4Chufan Xiao5Zhicheng Du6Zhicheng Du7Chenyao Jiang8Chenyao Jiang9Xi Yuan10Xi Yuan11Qiuyue Hu12Qiuyue Hu13Shiyao Zhai14Shiyao Zhai15Lulu Xu16Lulu Xu17Changyue Liu18Changyue Liu19Xiaoyun Zhong20Xiaoyun Zhong21Haifei Guan22Haifei Guan23Muhammad Hassan24Muhammad Hassan25Ijaz Gul26Ijaz Gul27Vijay Pandey28Vijay Pandey29Xinhui Xing30Xinhui Xing31Can Yang Zhang32Can Yang Zhang33Qian He34Qian He35Peiwu Qin36Peiwu Qin37Center of Precision Medicine and Healthcare, Tsinghua-Berkeley Shenzhen Institute, Shenzhen, Guangdong, ChinaInstitute of Biopharmaceutics and Health Engineering, Tsinghua Shenzhen International Graduate School, Shenzhen, Guangdong, ChinaCenter of Precision Medicine and Healthcare, Tsinghua-Berkeley Shenzhen Institute, Shenzhen, Guangdong, ChinaInstitute of Biopharmaceutics and Health Engineering, Tsinghua Shenzhen International Graduate School, Shenzhen, Guangdong, ChinaCenter of Precision Medicine and Healthcare, Tsinghua-Berkeley Shenzhen Institute, Shenzhen, Guangdong, ChinaInstitute of Biopharmaceutics and Health Engineering, Tsinghua Shenzhen International Graduate School, Shenzhen, Guangdong, ChinaCenter of Precision Medicine and Healthcare, Tsinghua-Berkeley Shenzhen Institute, Shenzhen, Guangdong, ChinaInstitute of Biopharmaceutics and Health Engineering, Tsinghua Shenzhen International Graduate School, Shenzhen, Guangdong, ChinaCenter of Precision Medicine and Healthcare, Tsinghua-Berkeley Shenzhen Institute, Shenzhen, Guangdong, ChinaInstitute of Biopharmaceutics and Health Engineering, Tsinghua Shenzhen International Graduate School, Shenzhen, Guangdong, ChinaCenter of Precision Medicine and Healthcare, Tsinghua-Berkeley Shenzhen Institute, Shenzhen, Guangdong, ChinaInstitute of Biopharmaceutics and Health Engineering, Tsinghua Shenzhen International Graduate School, Shenzhen, Guangdong, ChinaCenter of Precision Medicine and Healthcare, Tsinghua-Berkeley Shenzhen Institute, Shenzhen, Guangdong, ChinaInstitute of Biopharmaceutics and Health Engineering, Tsinghua Shenzhen International Graduate School, Shenzhen, Guangdong, ChinaCenter of Precision Medicine and Healthcare, Tsinghua-Berkeley Shenzhen Institute, Shenzhen, Guangdong, ChinaInstitute of Biopharmaceutics and Health Engineering, Tsinghua Shenzhen International Graduate School, Shenzhen, Guangdong, ChinaCenter of Precision Medicine and Healthcare, Tsinghua-Berkeley Shenzhen Institute, Shenzhen, Guangdong, ChinaInstitute of Biopharmaceutics and Health Engineering, Tsinghua Shenzhen International Graduate School, Shenzhen, Guangdong, ChinaCenter of Precision Medicine and Healthcare, Tsinghua-Berkeley Shenzhen Institute, Shenzhen, Guangdong, ChinaInstitute of Biopharmaceutics and Health Engineering, Tsinghua Shenzhen International Graduate School, Shenzhen, Guangdong, ChinaCenter of Precision Medicine and Healthcare, Tsinghua-Berkeley Shenzhen Institute, Shenzhen, Guangdong, ChinaInstitute of Biopharmaceutics and Health Engineering, Tsinghua Shenzhen International Graduate School, Shenzhen, Guangdong, ChinaCenter of Precision Medicine and Healthcare, Tsinghua-Berkeley Shenzhen Institute, Shenzhen, Guangdong, ChinaInstitute of Biopharmaceutics and Health Engineering, Tsinghua Shenzhen International Graduate School, Shenzhen, Guangdong, ChinaCenter of Precision Medicine and Healthcare, Tsinghua-Berkeley Shenzhen Institute, Shenzhen, Guangdong, ChinaInstitute of Biopharmaceutics and Health Engineering, Tsinghua Shenzhen International Graduate School, Shenzhen, Guangdong, ChinaCenter of Precision Medicine and Healthcare, Tsinghua-Berkeley Shenzhen Institute, Shenzhen, Guangdong, ChinaInstitute of Biopharmaceutics and Health Engineering, Tsinghua Shenzhen International Graduate School, Shenzhen, Guangdong, ChinaCenter of Precision Medicine and Healthcare, Tsinghua-Berkeley Shenzhen Institute, Shenzhen, Guangdong, ChinaInstitute of Biopharmaceutics and Health Engineering, Tsinghua Shenzhen International Graduate School, Shenzhen, Guangdong, ChinaCenter of Precision Medicine and Healthcare, Tsinghua-Berkeley Shenzhen Institute, Shenzhen, Guangdong, ChinaInstitute of Biopharmaceutics and Health Engineering, Tsinghua Shenzhen International Graduate School, Shenzhen, Guangdong, ChinaCenter of Precision Medicine and Healthcare, Tsinghua-Berkeley Shenzhen Institute, Shenzhen, Guangdong, ChinaInstitute of Biopharmaceutics and Health Engineering, Tsinghua Shenzhen International Graduate School, Shenzhen, Guangdong, ChinaCenter of Precision Medicine and Healthcare, Tsinghua-Berkeley Shenzhen Institute, Shenzhen, Guangdong, ChinaInstitute of Biopharmaceutics and Health Engineering, Tsinghua Shenzhen International Graduate School, Shenzhen, Guangdong, ChinaCenter of Precision Medicine and Healthcare, Tsinghua-Berkeley Shenzhen Institute, Shenzhen, Guangdong, ChinaInstitute of Biopharmaceutics and Health Engineering, Tsinghua Shenzhen International Graduate School, Shenzhen, Guangdong, ChinaIntegrating artificial intelligence with SARS-CoV-2 diagnostics can help in the timely execution of pandemic control and monitoring plans. To improve the efficiency of the diagnostic process, this study aims to classify fluorescent images via traditional machine learning and deep learning-based transfer learning. A previous study reported a CRISPR-Cas13a system combined with total internal reflection fluorescence microscopy (TIRFM) to detect the existence and concentrations of SARS-CoV-2 by fluorescent images. However, the lack of professional software and excessive manual labor hinder the practicability of the system. Here, we construct a fluorescent image dataset and develop an AI-boosted CRISPR-Cas13a and total internal reflection fluorescence microscopy system for the rapid diagnosis of SARS-CoV-2. Our study proposes Fluorescent Images Classification Transfer learning based on DenseNet-121 (FICTransDense), an approach that uses TIRF images (before and after sample introduction, respectively) for preprocessing, including outlier exclusion and setting and division preprocessing (i.e., SDP). Classification results indicate that the FICTransDense and Decision Tree algorithms outperform other approaches on the SDP dataset. Most of the algorithms benefit from the proposed SDP technique in terms of Accuracy, Recall, F1 Score, and Precision. The use of AI-boosted CRISPR-Cas13a and TIRFM systems facilitates rapid monitoring and diagnosis of SARS-CoV-2.https://www.frontiersin.org/articles/10.3389/fsens.2022.1015223/fullCRISPR-Cas13aTIRFMmachine learningtransfer learningDensenet-121SARS-CoV-2 |
| spellingShingle | Likun Zhang Likun Zhang Zhengyang Lei Zhengyang Lei Chufan Xiao Chufan Xiao Zhicheng Du Zhicheng Du Chenyao Jiang Chenyao Jiang Xi Yuan Xi Yuan Qiuyue Hu Qiuyue Hu Shiyao Zhai Shiyao Zhai Lulu Xu Lulu Xu Changyue Liu Changyue Liu Xiaoyun Zhong Xiaoyun Zhong Haifei Guan Haifei Guan Muhammad Hassan Muhammad Hassan Ijaz Gul Ijaz Gul Vijay Pandey Vijay Pandey Xinhui Xing Xinhui Xing Can Yang Zhang Can Yang Zhang Qian He Qian He Peiwu Qin Peiwu Qin AI-boosted CRISPR-Cas13a and total internal reflection fluorescence microscopy system for SARS-CoV-2 detection Frontiers in Sensors CRISPR-Cas13a TIRFM machine learning transfer learning Densenet-121 SARS-CoV-2 |
| title | AI-boosted CRISPR-Cas13a and total internal reflection fluorescence microscopy system for SARS-CoV-2 detection |
| title_full | AI-boosted CRISPR-Cas13a and total internal reflection fluorescence microscopy system for SARS-CoV-2 detection |
| title_fullStr | AI-boosted CRISPR-Cas13a and total internal reflection fluorescence microscopy system for SARS-CoV-2 detection |
| title_full_unstemmed | AI-boosted CRISPR-Cas13a and total internal reflection fluorescence microscopy system for SARS-CoV-2 detection |
| title_short | AI-boosted CRISPR-Cas13a and total internal reflection fluorescence microscopy system for SARS-CoV-2 detection |
| title_sort | ai boosted crispr cas13a and total internal reflection fluorescence microscopy system for sars cov 2 detection |
| topic | CRISPR-Cas13a TIRFM machine learning transfer learning Densenet-121 SARS-CoV-2 |
| url | https://www.frontiersin.org/articles/10.3389/fsens.2022.1015223/full |
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