TY  - JOUR
AU  - Bermejo-Peláez, David
AU  - Marcos-Mencía, Daniel
AU  - Álamo, Elisa
AU  - Pérez-Panizo, Nuria
AU  - Mousa, Adriana
AU  - Dacal, Elena
AU  - Lin, Lin
AU  - Vladimirov, Alexander
AU  - Cuadrado, Daniel
AU  - Mateos-Nozal, Jesús
AU  - Galán, Juan Carlos
AU  - Romero-Hernandez, Beatriz
AU  - Cantón, Rafael
AU  - Luengo-Oroz, Miguel
AU  - Rodriguez-Dominguez, Mario
PY  - 2022
DA  - 2022/12/30
TI  - A Smartphone-Based Platform Assisted by Artificial Intelligence for Reading and Reporting Rapid Diagnostic Tests: Evaluation Study in SARS-CoV-2 Lateral Flow Immunoassays
JO  - JMIR Public Health Surveill
SP  - e38533
VL  - 8
IS  - 12
KW  - rapid diagnostic test
KW  - artificial intelligence
KW  - AI
KW  - telemedicine platform
KW  - COVID-19
KW  - rapid test
KW  - diagnostics
KW  - epidemiology
KW  - surveillance
KW  - automatic
KW  - automated
KW  - tracking
AB  - 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. 
SN  - 2369-2960
UR  - https://publichealth.jmir.org/2022/12/e38533
UR  - https://doi.org/10.2196/38533
UR  - http://www.ncbi.nlm.nih.gov/pubmed/36265136
DO  - 10.2196/38533
ID  - info:doi/10.2196/38533
ER  -