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Kumar S, Ko T, Chae Y, Jang Y, Lee I, Lee A, Shin S, Nam MH, Kim BS, Jun HS, Seo S. Proof-of-Concept: Smartphone- and Cloud-Based Artificial Intelligence Quantitative Analysis System (SCAISY) for SARS-CoV-2-Specific IgG Antibody Lateral Flow Assays. Biosensors (Basel) 2023; 13:623. [PMID: 37366988 DOI: 10.3390/bios13060623] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/20/2023] [Revised: 05/31/2023] [Accepted: 06/02/2023] [Indexed: 06/28/2023]
Abstract
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, p = 0.008; Pearson correlation coefficient: 0.56, p = 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.
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Affiliation(s)
- Samir Kumar
- Department of Electronics and Information Engineering, Korea University, Sejong 30019, Republic of Korea
| | - Taewoo Ko
- Department of Electronics and Information Engineering, Korea University, Sejong 30019, Republic of Korea
| | | | - Yuyeon Jang
- Department of Biotechnology and Bioinformatics, Korea University, Sejong 30019, Republic of Korea
| | - Inha Lee
- Department of Biotechnology and Bioinformatics, Korea University, Sejong 30019, Republic of Korea
| | - Ahyeon Lee
- Department of Electronics and Information Engineering, Korea University, Sejong 30019, Republic of Korea
| | - Sanghoon Shin
- Department of Electronics and Information Engineering, Korea University, Sejong 30019, Republic of Korea
| | - Myung-Hyun Nam
- Department of Laboratory Medicine, Anam Hospital, Korea University College of Medicine, Seoul 02841, Republic of Korea
| | - Byung Soo Kim
- Department of Hematology, Anam Hospital, Korea University College of Medicine, Seoul 02841, Republic of Korea
| | - Hyun Sik Jun
- Department of Biotechnology and Bioinformatics, Korea University, Sejong 30019, Republic of Korea
| | - Sungkyu Seo
- Department of Electronics and Information Engineering, Korea University, Sejong 30019, Republic of Korea
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