tetano
Editor, Senior Moderator
Cell Rep Med
. 2022 Sep 27;100784.
doi: 10.1016/j.xcrm.2022.100784. Online ahead of print.
Machine learning for determining lateral flow device results for testing of SARS-CoV-2 infection in asymptomatic populations
LFD AI Consortium. Electronic address: [email]a.beggs@bham.ac.uk[/email]; LFD AI Consortium
Collaborators
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% 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.
Keywords: AI; COVID-19; lateral flow device; machine learning.
. 2022 Sep 27;100784.
doi: 10.1016/j.xcrm.2022.100784. Online ahead of print.
Machine learning for determining lateral flow device results for testing of SARS-CoV-2 infection in asymptomatic populations
LFD AI Consortium. Electronic address: [email]a.beggs@bham.ac.uk[/email]; LFD AI Consortium
Collaborators
- PMID: 36240756
- PMCID: PMC9513327
- DOI: 10.1016/j.xcrm.2022.100784
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% 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.
Keywords: AI; COVID-19; lateral flow device; machine learning.