An approach to use machine learning to optimize paper immunoassays for SARS-CoV-2 IgG and IgM antibodies

Optimizing paper immunoassay conditions for diagnostic accuracy is often achieved by tuning running conditions in a trial and error manner. We developed an approach to use machine learning (ML) in the optimization process, demonstrating it on a COVID-19 assay to detect IgG and IgM antibodies for both SARS CoV-2 spike and nucleocapsid proteins. The multiplexed test had a multicolor readout by using red and blue gold nanoparticles. Spike and nucleocapsid proteins were immobilized on a nitrocellulose strip at different locations, and the assay was run with red nanoparticles conjugated to anti-IgG and blue nanostars conjugated to anti-IgM. The spatial location of the signal indicated whether the antibody present was anti-spike or anti-nucleocapsid, and the test area color indicated the antibody type (IgG vs. IgM). Linear discriminant analysis (LDA) and ML were used to evaluate the test accuracy, and then used iteratively to modify running conditions (presence of quencher molecules, nanoparticle types, washes) until the test accuracy reached 100%. The resulting assay could be trained to distinguish between 9 different antibody profiles indicative of different disease cases (prior infection vs. vaccinated, early/mid/late stage post infection). Results show that supervised learning can accelerate test development, and that using the test as a selective array rather than a specific sensor could enable rapid immunoassays to obtain more complex information. Optimizing paper immunoassay conditions for diagnostic accuracy is often achieved by tuning running conditions in a trial and error manner. We report the use of machine learning to optimize an assay for SARS-CoV-2 IgG and IgM antibodies.