Proof-of-Concept: Smartphone- and Cloud-Based Artificial Intelligence Quantitative Analysis System (SCAISY) for SARS-CoV-2-Specific IgG Antibody Lateral Flow Assays

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.

Lateral Flow Assay: A Summary of Recent Progress for Improving Assay Performance

“…Liu et al exhaustively discussed approaches for augmenting the sensitivity and specificity of LFAs [ 69 ]. In addition to standard reader devices, smartphone readers are also emerging as favorable alternative systems for improving the utility of POC tests in which detailed information or details are provided in other studies [ 11 , 81 , 82 , 83 , 84 ]. Using a smartphone, it is possible to quantify the test line color intensity via a camera or a more sophisticated photodetector to estimate the number of target molecules in the specimen.…”

A Paper-Based Multiplexed Serological Test to Monitor Immunity against SARS-COV-2 Using Machine Learning

“…To date, lateral flow assays (LFAs) have been widely used for COVID-19 serology, offering rapid and simple point-of-care (POC) solutions for detecting antibodies. − LFAs typically focus on the detection of a single type of antibody (IgG or IgM) by using one type of SARS-CoV-2 protein (S-protein or N-protein), and this design limits the ability to differentiate IgG and IgM antibody levels in a given sample. This drawback is rooted in using a single type of SARS-CoV-2 protein within the LFA platform, where IgG can have affinity for both the S-protein and the N-protein; therefore, LFAs may not provide sufficient specificity to differentiate between antibodies generated from infection and those induced by vaccination.…”