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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.
Bibliographical noteFunding Information:
The study was funded by NHS Test and Trace. A.D.B. is funded by an CRUK Advanced Clinician Scientist Award (C31641/A23923). The funder had input into design and conduct but not reporting of the study. Study conception and design A.D.B. and the LFD AI Consortium; algorithmic design and implementation, the LFD AI Consortium; laboratory work: A.D.B. and the LFD AI Consortium; statistical analysis, A.D.B. and the LFD AI Consortium; writing of the manuscript, A.D.B. and the LFD AI Consortium. All authors had access to the data presented in the manuscript. The authors declare no competing interests. We worked to ensure gender balance in the recruitment of human subjects. We worked to ensure ethnic or other types of diversity in the recruitment of human subjects. We worked to ensure that the study questionnaires were prepared in an inclusive way. One or more of the authors of this paper self-identifies as an underrepresented ethnic minority in science. One or more of the authors of this paper self-identifies as living with a disability.
© 2022 The Author(s)
- Sensitivity and Specificity
- COVID-19 Testing
- Machine Learning
ASJC Scopus subject areas
- Biochemistry, Genetics and Molecular Biology(all)