• Meshkinfamfard, Sepehr
• Atchison, Christina
• Wong, Nathan
• Moshe, Maya
• Mckendry, Rachel
• Cooke, Graham
• Elliott, Paul
• Tanaka, Reiko
• Dai, Tianhong
• Pignatelli, Eduardo
• Turbe, Valerian
• Bardanzellu, Alessia
• Bharath, Anil A.
• Darzi, Ara
• Barclay, Wendy
• Whitaker, Matthew
• Ward, Helen

Abstract

<jats:title>Abstract</jats:title><jats:sec><jats:title>Background</jats:title><jats:p>Lateral flow immunoassays (LFIAs) are being used worldwide for COVID-19 mass testing and antibody prevalence studies. Relatively simple to use and low cost, these tests can be self-administered at home, but rely on subjective interpretation of a test line by eye, risking false positives and false negatives. Here, we report on the development of ALFA (Automated Lateral Flow Analysis) to improve reported sensitivity and specificity.</jats:p></jats:sec><jats:sec><jats:title>Methods</jats:title><jats:p>Our computational pipeline uses machine learning, computer vision techniques and signal processing algorithms to analyse images of the Fortress LFIA SARS-CoV-2 antibody self-test, and subsequently classify results as invalid, IgG negative and IgG positive. A large image library of 595,339 participant-submitted test photographs was created as part of the REACT-2 community SARS-CoV-2 antibody prevalence study in England, UK. Alongside ALFA, we developed an analysis toolkit which could also detect device blood leakage issues.</jats:p></jats:sec><jats:sec><jats:title>Results</jats:title><jats:p>Automated analysis showed substantial agreement with human experts (Cohen’s kappa 0.90–0.97) and performed consistently better than study participants, particularly for weak positive IgG results. Specificity (98.7–99.4%) and sensitivity (90.1–97.1%) were high compared with visual interpretation by human experts (ranges due to the varying prevalence of weak positive IgG tests in datasets).</jats:p></jats:sec><jats:sec><jats:title>Conclusions</jats:title><jats:p>Given the potential for LFIAs to be used at scale in the COVID-19 response (for both antibody and antigen testing), even a small improvement in the accuracy of the algorithms could impact the lives of millions of people by reducing the risk of false-positive and false-negative result read-outs by members of the public. Our findings support the use of machine learning-enabled automated reading of at-home antibody lateral flow tests as a tool for improved accuracy for population-level community surveillance.</jats:p></jats:sec>

Topics

• size-exclusion chromatography
• machine learning