Open AccessUsing artificial intelligence to improve COVID-19 rapid diagnostic test result interpretation
Author(s) -
David-A. Mendels,
Laurent Dortet,
Cécile Emeraud,
Saoussen Oueslati,
Delphine Girlich,
Jean-Baptiste Ronat,
Sandrine Bernabeu,
Silvestre Bahi,
G Atkinson,
Thierry Naas
Publication year - 2021
Publication title -
proceedings of the national academy of sciences of the united states of america
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 5.011
H-Index - 771
eISSN - 1091-6490
pISSN - 0027-8424
DOI - 10.1073/pnas.2019893118
Subject(s) - covid-19 , computer science , artificial intelligence , test (biology) , diagnostic test , binary classification , classifier (uml) , machine learning , natural language processing , medicine , pathology , pediatrics , support vector machine , biology , infectious disease (medical specialty) , paleontology , disease , outbreak
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.