Proof-of-Concept: Smartphone- and Cloud-Based Artificial Intelligence Quantitative Analysis System (SCAISY) for SARS-CoV-2-Specific IgG Antibody Lateral Flow Assays
Smartphone-based point-of-care testing (POCT) is rapidly emerging as an alternative to traditional screening and laboratory testing, particularly in resource-limited settings. In this proof-of-concept study, we present a smartphone- and cloud-based artificial intelligence quantitative analysis syste...
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-06-01
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| Series: | Biosensors |
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| Online Access: | https://www.mdpi.com/2079-6374/13/6/623 |
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| author | Samir Kumar Taewoo Ko Yeonghun Chae Yuyeon Jang Inha Lee Ahyeon Lee Sanghoon Shin Myung-Hyun Nam Byung Soo Kim Hyun Sik Jun Sungkyu Seo |
| author_facet | Samir Kumar Taewoo Ko Yeonghun Chae Yuyeon Jang Inha Lee Ahyeon Lee Sanghoon Shin Myung-Hyun Nam Byung Soo Kim Hyun Sik Jun Sungkyu Seo |
| author_sort | Samir Kumar |
| collection | DOAJ |
| description | Smartphone-based point-of-care testing (POCT) is rapidly emerging as an alternative to traditional screening and laboratory testing, particularly in resource-limited settings. In this proof-of-concept study, we present a smartphone- and cloud-based artificial intelligence quantitative analysis system (SCAISY) for relative quantification of SARS-CoV-2-specific IgG antibody lateral flow assays that enables rapid evaluation (<60 s) of test strips. By capturing an image with a smartphone camera, SCAISY quantitatively analyzes antibody levels and provides results to the user. We analyzed changes in antibody levels over time in more than 248 individuals, including vaccine type, number of doses, and infection status, with a standard deviation of less than 10%. We also tracked antibody levels in six participants before and after SARS-CoV-2 infection. Finally, we examined the effects of lighting conditions, camera angle, and smartphone type to ensure consistency and reproducibility. We found that images acquired between 45° and 90° provided accurate results with a small standard deviation and that all illumination conditions provided essentially identical results within the standard deviation. A statistically significant correlation was observed (Spearman correlation coefficient: 0.59, <i>p</i> = 0.008; Pearson correlation coefficient: 0.56, <i>p</i> = 0.012) between the OD450 values of the enzyme-linked immunosorbent assay and the antibody levels obtained by SCAISY. This study suggests that SCAISY is a simple and powerful tool for real-time public health surveillance, enabling the acceleration of quantifying SARS-CoV-2-specific antibodies generated by either vaccination or infection and tracking of personal immunity levels. |
| first_indexed | 2024-03-11T02:42:00Z |
| format | Article |
| id | doaj.art-9d3389196152413ebd6662589cf73204 |
| institution | Directory Open Access Journal |
| issn | 2079-6374 |
| language | English |
| last_indexed | 2024-03-11T02:42:00Z |
| publishDate | 2023-06-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Biosensors |
| spelling | doaj.art-9d3389196152413ebd6662589cf732042023-11-18T09:32:51ZengMDPI AGBiosensors2079-63742023-06-0113662310.3390/bios13060623Proof-of-Concept: Smartphone- and Cloud-Based Artificial Intelligence Quantitative Analysis System (SCAISY) for SARS-CoV-2-Specific IgG Antibody Lateral Flow AssaysSamir Kumar0Taewoo Ko1Yeonghun Chae2Yuyeon Jang3Inha Lee4Ahyeon Lee5Sanghoon Shin6Myung-Hyun Nam7Byung Soo Kim8Hyun Sik Jun9Sungkyu Seo10Department of Electronics and Information Engineering, Korea University, Sejong 30019, Republic of KoreaDepartment of Electronics and Information Engineering, Korea University, Sejong 30019, Republic of KoreaSeason Co., Ltd., Sejong 30127, Republic of KoreaDepartment of Biotechnology and Bioinformatics, Korea University, Sejong 30019, Republic of KoreaDepartment of Biotechnology and Bioinformatics, Korea University, Sejong 30019, Republic of KoreaDepartment of Electronics and Information Engineering, Korea University, Sejong 30019, Republic of KoreaDepartment of Electronics and Information Engineering, Korea University, Sejong 30019, Republic of KoreaDepartment of Laboratory Medicine, Anam Hospital, Korea University College of Medicine, Seoul 02841, Republic of KoreaDepartment of Hematology, Anam Hospital, Korea University College of Medicine, Seoul 02841, Republic of KoreaDepartment of Biotechnology and Bioinformatics, Korea University, Sejong 30019, Republic of KoreaDepartment of Electronics and Information Engineering, Korea University, Sejong 30019, Republic of KoreaSmartphone-based point-of-care testing (POCT) is rapidly emerging as an alternative to traditional screening and laboratory testing, particularly in resource-limited settings. In this proof-of-concept study, we present a smartphone- and cloud-based artificial intelligence quantitative analysis system (SCAISY) for relative quantification of SARS-CoV-2-specific IgG antibody lateral flow assays that enables rapid evaluation (<60 s) of test strips. By capturing an image with a smartphone camera, SCAISY quantitatively analyzes antibody levels and provides results to the user. We analyzed changes in antibody levels over time in more than 248 individuals, including vaccine type, number of doses, and infection status, with a standard deviation of less than 10%. We also tracked antibody levels in six participants before and after SARS-CoV-2 infection. Finally, we examined the effects of lighting conditions, camera angle, and smartphone type to ensure consistency and reproducibility. We found that images acquired between 45° and 90° provided accurate results with a small standard deviation and that all illumination conditions provided essentially identical results within the standard deviation. A statistically significant correlation was observed (Spearman correlation coefficient: 0.59, <i>p</i> = 0.008; Pearson correlation coefficient: 0.56, <i>p</i> = 0.012) between the OD450 values of the enzyme-linked immunosorbent assay and the antibody levels obtained by SCAISY. This study suggests that SCAISY is a simple and powerful tool for real-time public health surveillance, enabling the acceleration of quantifying SARS-CoV-2-specific antibodies generated by either vaccination or infection and tracking of personal immunity levels.https://www.mdpi.com/2079-6374/13/6/623smartphonepoint-of-care testinglateral flow assayartificial intelligenceSARS-CoV-2IgG antibody |
| spellingShingle | Samir Kumar Taewoo Ko Yeonghun Chae Yuyeon Jang Inha Lee Ahyeon Lee Sanghoon Shin Myung-Hyun Nam Byung Soo Kim Hyun Sik Jun Sungkyu Seo Proof-of-Concept: Smartphone- and Cloud-Based Artificial Intelligence Quantitative Analysis System (SCAISY) for SARS-CoV-2-Specific IgG Antibody Lateral Flow Assays Biosensors smartphone point-of-care testing lateral flow assay artificial intelligence SARS-CoV-2 IgG antibody |
| title | Proof-of-Concept: Smartphone- and Cloud-Based Artificial Intelligence Quantitative Analysis System (SCAISY) for SARS-CoV-2-Specific IgG Antibody Lateral Flow Assays |
| title_full | Proof-of-Concept: Smartphone- and Cloud-Based Artificial Intelligence Quantitative Analysis System (SCAISY) for SARS-CoV-2-Specific IgG Antibody Lateral Flow Assays |
| title_fullStr | Proof-of-Concept: Smartphone- and Cloud-Based Artificial Intelligence Quantitative Analysis System (SCAISY) for SARS-CoV-2-Specific IgG Antibody Lateral Flow Assays |
| title_full_unstemmed | Proof-of-Concept: Smartphone- and Cloud-Based Artificial Intelligence Quantitative Analysis System (SCAISY) for SARS-CoV-2-Specific IgG Antibody Lateral Flow Assays |
| title_short | Proof-of-Concept: Smartphone- and Cloud-Based Artificial Intelligence Quantitative Analysis System (SCAISY) for SARS-CoV-2-Specific IgG Antibody Lateral Flow Assays |
| title_sort | proof of concept smartphone and cloud based artificial intelligence quantitative analysis system scaisy for sars cov 2 specific igg antibody lateral flow assays |
| topic | smartphone point-of-care testing lateral flow assay artificial intelligence SARS-CoV-2 IgG antibody |
| url | https://www.mdpi.com/2079-6374/13/6/623 |
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