Machine learning for determining lateral flow device results for testing of SARS-CoV-2 infection in asymptomatic populations
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.
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.