Article ; Online: Using artificial intelligence to improve COVID-19 rapid diagnostic test result interpretation.
Proceedings of the National Academy of Sciences of the United States of America
2021 Volume 118, Issue 12
Abstract: 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- ... ...
Abstract | 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. |
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MeSH term(s) | COVID-19/diagnosis ; COVID-19 Serological Testing ; Humans ; Machine Learning ; Mobile Applications ; SARS-CoV-2 |
Language | English |
Publishing date | 2021-03-05 |
Publishing country | United States |
Document type | Journal Article ; Research Support, Non-U.S. Gov't |
ZDB-ID | 209104-5 |
ISSN | 1091-6490 ; 0027-8424 |
ISSN (online) | 1091-6490 |
ISSN | 0027-8424 |
DOI | 10.1073/pnas.2019893118 |
Database | MEDical Literature Analysis and Retrieval System OnLINE |
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