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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.

Original publication

DOI

10.1016/j.xcrm.2022.100784

Type

Journal article

Journal

Cell reports. Medicine

Publication Date

10/2022

Volume

3

Keywords

LFD AI Consortium. Electronic address: a.beggs@bham.ac.uk, LFD AI Consortium, Humans, Sensitivity and Specificity, Machine Learning, COVID-19, SARS-CoV-2, COVID-19 Testing