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

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

The rapid spread of SARS-CoV-2 caused the COVID-19 pandemic and accelerated vaccine development to prevent the spread of the virus and control the disease. Given the sustained high infectivity and evolution of SARS-CoV-2, there is an ongoing interest in developing COVID-19 serology tests to monitor population-level immunity. To address this critical need, we designed a paper-based multiplexed vertical flow assay (xVFA) using five structural proteins of SARS-CoV-2, detecting IgG and IgM antibodies to monitor changes in COVID-19 immunity levels. Our platform not only tracked longitudinal immunity levels but also categorized COVID-19 immunity into three groups: protected, unprotected, and infected, based on the levels of IgG and IgM antibodies. We operated two xVFAs in parallel to detect IgG and IgM antibodies using a total of 40 μL of human serum sample in <20 min per test. After the assay, images of the paper-based sensor panel were captured using a mobile phone-based custom-designed optical reader and then processed by a neural network-based serodiagnostic algorithm. The serodiagnostic algorithm was trained with 120 measurements/tests and 30 serum samples from 7 randomly selected individuals and was blindly tested with 31 serum samples from 8 different individuals, collected before vaccination as well as after vaccination or infection, achieving an accuracy of 89.5%. The competitive performance of the xVFA, along with its portability, cost-effectiveness, and rapid operation, makes it a promising computational point-of-care (POC) serology test for monitoring COVID-19 immunity, aiding in timely decisions on the administration of booster vaccines and general public health policies to protect vulnerable populations.

The utility of smartphone-based quantitative analysis of SARS-CoV-2-specific antibody lateral flow assays

OBJECTIVES

Although antibody measurements using lateral flow assay (LFA) kits are convenient, they usually require a specialized reader for quantification. However, a smartphone-based quantification application can be used as a reader for LFA kits. We investigated the quantification ability of the application for SARS-CoV-2-specific antibodies.

METHODS

Eight hundred frozen serum samples from 100 healthcare professionals who received a COVID-19 vaccine were analyzed. Images of assayed LFA kits were obtained using a smartphone camera. We determined whether the ratio of color density of the test and control lines of spike protein IgG correlated with chemiluminescent immunoassay-measured titers.

RESULTS

Spike protein IgG correlated well with the quantification results of the LFA kits using the application installed on a smartphone (r = 0.886).

CONCLUSION

Our results suggest that smartphone-based quantitative analysis of LFA kits enables the quantification of anti-SARS-CoV-2 IgG without special devices, enabling point-of-care assessment of acquired humoral immunity in various settings.