1. Machine learning for determining lateral flow device results for testing of SARS-CoV-2 infection in asymptomatic populations
- Author
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Beggs, AD, Caiado, CCS, Branigan, M, Lewis-Borman, P, Patel, N, Fowler, T, Dijkstra, A, Chudzik, P, Yousefi, P, Javer, A, Van Meurs, B, Tarassenko, L, Irving, B, Whalley, C, Lal, N, Robbins, H, Leung, E, Lee, L, Banathy, R, and Consortium, LFD AI
- Subjects
Machine Learning ,COVID-19 Testing ,SARS-CoV-2 ,Humans ,COVID-19 ,Sensitivity and Specificity ,General Biochemistry, Genetics and Molecular Biology - Abstract
Rapid antigen tests in the form of lateral flow devices (LFDs) allow testing of a large population for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). To reduce the variability in device interpretation, we show the design and testing of an artifical intelligence (AI) algorithm based on machine learning. The machine learning (ML) algorithm is trained on a combination of artificially hybridized LFDs and LFD data linked to quantitative real-time PCR results. Participants are recruited from assisted test sites (ATSs) and health care workers undertaking self-testing, and images are analyzed using the ML algorithm. A panel of trained clinicians is used to resolve discrepancies. In total, 115,316 images are returned. In the ATS substudy, sensitivity increased from 92.08% to 97.6% and specificity from 99.85% to 99.99%. In the self-read substudy, sensitivity increased from 16.00% to 100% and specificity from 99.15% to 99.40%. An ML-based classifier of LFD results outperforms human reads in assisted testing sites and self-reading.
- Published
- 2022