Using artificial intelligence to improve COVID-19 rapid diagnostic test result interpretation.
SARS-CoV-2
machine learning
smartphone application
Journal
Proceedings of the National Academy of Sciences of the United States of America
ISSN: 1091-6490
Titre abrégé: Proc Natl Acad Sci U S A
Pays: United States
ID NLM: 7505876
Informations de publication
Date de publication:
23 03 2021
23 03 2021
Historique:
entrez:
6
3
2021
pubmed:
7
3
2021
medline:
16
3
2021
Statut:
ppublish
Résumé
Serological rapid diagnostic tests (RDTs) are widely used across pathologies, often providing users a simple, binary result (positive or negative) in as little as 5 to 20 min. Since the beginning of the COVID-19 pandemic, new RDTs for identifying SARS-CoV-2 have rapidly proliferated. However, these seemingly easy-to-read tests can be highly subjective, and interpretations of the visible "bands" of color that appear (or not) in a test window may vary between users, test models, and brands. We developed and evaluated the accuracy/performance of a smartphone application (xRCovid) that uses machine learning to classify SARS-CoV-2 serological RDT results and reduce reading ambiguities. Across 11 COVID-19 RDT models, the app yielded 99.3% precision compared to reading by eye. Using the app replaces the uncertainty from visual RDT interpretation with a smaller uncertainty of the image classifier, thereby increasing confidence of clinicians and laboratory staff when using RDTs, and creating opportunities for patient self-testing.
Identifiants
pubmed: 33674422
pii: 2019893118
doi: 10.1073/pnas.2019893118
pmc: PMC7999948
pii:
doi:
Types de publication
Journal Article
Research Support, Non-U.S. Gov't
Langues
eng
Sous-ensembles de citation
IM
Informations de copyright
Copyright © 2021 the Author(s). Published by PNAS.
Déclaration de conflit d'intérêts
The authors declare no competing interest.
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