30 December 2022
rapid diagnostic test; artificial intelligence; AI; telemedicine platform; COVID-19; rapid test; diagnostics; epidemiology; surveillance; automatic; automated; tracking
Bermejo-Peláez D, Marcos-Mencía D, Álamo E, Pérez-Panizo N, Mousa A, Dacal E, Lin L, Vladimirov A, Cuadrado D, Mateos-Nozal J, Galán JC, Romero-Hernandez B, Cantón R, Luengo-Oroz M, Rodriguez-Dominguez M
A Smartphone-Based Platform Assisted by Artificial Intelligence for Reading and Reporting Rapid Diagnostic Tests: Evaluation Study in SARS-CoV-2 Lateral Flow Immunoassays
JMIR Public Health Surveill 2022;8(12):e38533
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.