Machine learning for determining lateral flow device results for testing of SARS-CoV-2 infection in asymptomatic populations
Copyright © 2022 The Author(s). Published by Elsevier Inc. All rights reserved..
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.
| Medienart: |
E-Artikel |
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| Erscheinungsjahr: |
2022 |
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| Erschienen: |
2022 |
| Enthalten in: |
Zur Gesamtaufnahme - volume:3 |
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| Enthalten in: |
Cell reports. Medicine - 3(2022), 10 vom: 18. Okt., Seite 100784 |
| Sprache: |
Englisch |
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| Beteiligte Personen: |
LFD AI Consortium. Electronic address: a.beggs@bham.ac.uk [VerfasserIn] |
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| Links: |
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| Themen: |
AI |
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| Anmerkungen: |
Date Completed 21.10.2022 Date Revised 25.10.2022 published: Print-Electronic ISRCTN: ISRCTN30075312 Citation Status MEDLINE |
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| doi: |
10.1016/j.xcrm.2022.100784 |
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| funding: |
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| Förderinstitution / Projekttitel: |
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| PPN (Katalog-ID): |
NLM347557872 |
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| 520 | |a Copyright © 2022 The Author(s). Published by Elsevier Inc. All rights reserved. | ||
| 520 | |a 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 | ||
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| 650 | 4 | |a lateral flow device | |
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| 700 | 1 | |a Caiado, Camila C S |e investigator |4 oth | |
| 700 | 1 | |a Branigan, Mark |e investigator |4 oth | |
| 700 | 1 | |a Lewis-Borman, Paul |e investigator |4 oth | |
| 700 | 1 | |a Patel, Nishali |e investigator |4 oth | |
| 700 | 1 | |a Fowler, Tom |e investigator |4 oth | |
| 700 | 1 | |a Dijkstra, Anna |e investigator |4 oth | |
| 700 | 1 | |a Chudzik, Piotr |e investigator |4 oth | |
| 700 | 1 | |a Yousefi, Paria |e investigator |4 oth | |
| 700 | 1 | |a Javer, Avelino |e investigator |4 oth | |
| 700 | 1 | |a Van Meurs, Bram |e investigator |4 oth | |
| 700 | 1 | |a Tarassenko, Lionel |e investigator |4 oth | |
| 700 | 1 | |a Irving, Benjamin |e investigator |4 oth | |
| 700 | 1 | |a Whalley, Celina |e investigator |4 oth | |
| 700 | 1 | |a Lal, Neeraj |e investigator |4 oth | |
| 700 | 1 | |a Robbins, Helen |e investigator |4 oth | |
| 700 | 1 | |a Leung, Elaine |e investigator |4 oth | |
| 700 | 1 | |a Lee, Lennard |e investigator |4 oth | |
| 700 | 1 | |a Banathy, Robert |e investigator |4 oth | |
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