TY - JOUR
AU - Wong, Nathan Chun Kin
AU - Meshkinfamfard, Sepehr
AU - Turbe, Valerian
AU - Moshe, Maya
AU - Bardanzellu, Alessia
AU - Dai, Tianhong
AU - Pignatelli, Eduardo
AU - Barclay, Wendy S.
AU - Darzi, Ara
AU - Elliott, Paul
AU - Ward, Helen
AU - Tanaka, Reiko
AU - Cooke, Graham S.
AU - McKendry, Rachel
AU - Atchison, Christina
AU - Bharath, Anil A.
AU - Whitaker, Matthew
PY - 2022
DA - July
TI - Machine Learning to Support Visual Auditing of Home-based Lateral Flow Immunoassay Self-Test Results for SARS-CoV-2 Antibodies
JO - Communications Medicine
VL - 2
DO - https://doi.org/10.1038/s43856-022-00146-z
AB - 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.
PB - Springer Nature
UR - https://hdl.handle.net/2164/20296
KW - Coronavirus (COVID-19)
KW - Digital health and technology
ER