JMIR Publications

ABSTRACT

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 web telemedicine 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 from individuals with PCR-positive SARS-CoV-2 infection 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.