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

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% an

SUBMITTER: LFD AI Consortium. Electronic address: [e-mail] 

PROVIDER: S-EPMC9513327 | biostudies-literature | 2022 Oct

REPOSITORIES: biostudies-literature