@article{Wong_N-2022_120957,
title = {Machine Learning to Support Visual Auditing of Home-based Lateral Flow Immunoassay Self-Test Results for SARS-CoV-2 Antibodies},
author = {Wong, N. and Meshkinfamfard, S. and Turbe, V. and Moshe, M. and Bardanzellu, A. and Dai, T. and Pignatelli, E. and Barclay, W. and Darzi, A. and Elliott, P. and Ward, H. and Tanaka, R. and Cooke, G. and McKendry, R. and Atchison, C. and Bharath, A. and Whitaker, M.},
month = {jul},
year = {2022},
abstract = {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.},
volume = {2},
journal = {Communications Medicine},
publisher = {Springer Nature},
url = {https://doi.org/10.1038/s43856-022-00146-z},
}
