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

作者：Andrew David Beggs, Camila Caiado, Mark Branigan, Paul Lewis-Borman, Nishali Patel, Tom Fowler, Anna Dijkstra, Piotr Chudzik, Paria Yousefi, Avelino Javer, Bram Van Meurs, Lionel Tarassenko, Benjamin Irving, Celina Whalley, Neeraj K. Lal, Helen L. Robbins, Elaine Y L Leung, Lennard Y. W. Lee, Robert Banathy · 发表于：Cell Reports Medicine · 年份：2022 · DOI：10.1016/j.xcrm.2022.100784 · 被引用次数：23 · 研究领域：SARS-CoV-2 detection and testing、SARS-CoV-2 and COVID-19 Research、Biosensors and Analytical Detection

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.