Background: Rapid diagnostic tests (RDTs) are being widely used to manage COVID-19 pandemic. However, many results remain unreported or unconfirmed, altering a correct epidemiological surveillance.
Objective: Our aim was to evaluate an artificial intelligence-based smartphone app, connected to a cloud web platform, to automatically and objectively read RDT results and assess its impact on COVID-19 pandemic management.
Methods: Overall, 252 human sera were used to inoculate a total of 1165 RDTs for training and validation purposes. We then conducted two field studies to assess the performance on real-world scenarios by testing 172 antibody RDTs at two nursing homes and 96 antigen RDTs at one hospital emergency department.
Results: Field studies demonstrated high levels of sensitivity (100%) and specificity (94.4%, CI 92.8%-96.1%) for reading IgG band of COVID-19 antibody RDTs compared to visual readings from health workers. Sensitivity of detecting IgM test bands was 100%, and specificity was 95.8% (CI 94.3%-97.3%). All COVID-19 antigen RDTs were correctly read by the app.
Conclusions: The proposed reading system is automatic, reducing variability and uncertainty associated with RDTs interpretation and can be used to read different RDT brands. The web platform serves as a real-time epidemiological tracking tool and facilitates reporting of positive RDTs to relevant health authorities.
Keywords: AI; COVID-19; artificial intelligence; automated; automatic; diagnostics; epidemiology; rapid diagnostic test; rapid test; surveillance; telemedicine platform; tracking.
©David Bermejo-Peláez, Daniel Marcos-Mencía, Elisa Álamo, Nuria Pérez-Panizo, Adriana Mousa, Elena Dacal, Lin Lin, Alexander Vladimirov, Daniel Cuadrado, Jesús Mateos-Nozal, Juan Carlos Galán, Beatriz Romero-Hernandez, Rafael Cantón, Miguel Luengo-Oroz, Mario Rodriguez-Dominguez. Originally published in JMIR Public Health and Surveillance (https://publichealth.jmir.org), 30.12.2022.