Journal
PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA
Volume 118, Issue 12, Pages -Publisher
NATL ACAD SCIENCES
DOI: 10.1073/pnas.2019893118
Keywords
SARS-CoV-2; machine learning; smartphone application
Categories
Funding
- Assistance PubliqueHopitaux de Paris
- University Paris-Saclay
- Medecins Sans Frontieres
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This study developed a smartphone application called xRCovid, which uses machine learning to classify SARS-CoV-2 serological RDT results, improving the accuracy and reliability of RDT testing. The app can replace manual reading, reduce subjectivity in interpretation, and bring more confidence to patient self-testing and clinicians.
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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