In JMIR public health and surveillance
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 : To evaluate an artificial intelligence-based smartphone application, connected to a cloud web platform, to automatically and objectively read rapid diagnostic test (RDT) results and assess its impact on COVID-19 pandemic management.
METHODS : Overall, 252 human sera were used to inoculate a total of 1,165 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 antibodies 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 RDTs brands. The web platform serves as a real time epidemiological tracking tool and facilitates reporting of positive RDTs to relevant health authorities.
Bermejo-Peláez David, Marcos-Mencía Daniel, Álamo Elisa, Pérez-Panizo Nuria, Mousa Adriana, Dacal Elena, Lin Lin, Vladimirov Alexander, Cuadrado Daniel, Mateos-Nozal Jesús, Galán Juan Carlos, Romero-Hernandez Beatriz, Cantón Rafael, Luengo-Oroz Miguel, Rodriguez-Dominguez Mario