@Article{info:doi/10.2196/38533,
author="Bermejo-Pel{\'a}ez, David
and Marcos-Menc{\'i}a, Daniel
and {\'A}lamo, Elisa
and P{\'e}rez-Panizo, Nuria
and Mousa, Adriana
and Dacal, Elena
and Lin, Lin
and Vladimirov, Alexander
and Cuadrado, Daniel
and Mateos-Nozal, Jes{\'u}s
and Gal{\'a}n, Juan Carlos
and Romero-Hernandez, Beatriz
and Cant{\'o}n, Rafael
and Luengo-Oroz, Miguel
and Rodriguez-Dominguez, Mario",
title="A Smartphone-Based Platform Assisted by Artificial Intelligence for Reading and Reporting Rapid Diagnostic Tests: Evaluation Study in SARS-CoV-2 Lateral Flow Immunoassays",
journal="JMIR Public Health Surveill",
year="2022",
month="Dec",
day="30",
volume="8",
number="12",
pages="e38533",
keywords="rapid diagnostic test; artificial intelligence; AI; telemedicine platform; COVID-19; rapid test; diagnostics; epidemiology; surveillance; automatic; automated; tracking",
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: 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. ",
issn="2369-2960",
doi="10.2196/38533",
url="https://publichealth.jmir.org/2022/12/e38533",
url="https://doi.org/10.2196/38533",
url="http://www.ncbi.nlm.nih.gov/pubmed/36265136"
}