%0 Journal Article
%@ 2369-2960
%I JMIR Publications
%V 8
%N 12
%P e38533
%T A Smartphone-Based Platform Assisted by Artificial Intelligence for Reading and Reporting Rapid Diagnostic Tests: Evaluation Study in SARS-CoV-2 Lateral Flow Immunoassays
%A Bermejo-Peláez,David
%A Marcos-Mencía,Daniel
%A Álamo,Elisa
%A Pérez-Panizo,Nuria
%A Mousa,Adriana
%A Dacal,Elena
%A Lin,Lin
%A Vladimirov,Alexander
%A Cuadrado,Daniel
%A Mateos-Nozal,Jesús
%A Galán,Juan Carlos
%A Romero-Hernandez,Beatriz
%A Cantón,Rafael
%A Luengo-Oroz,Miguel
%A Rodriguez-Dominguez,Mario
%+ Spotlab, P.º de Juan XXIII, 36B, Madrid, 28040, Spain, 34 916256927, miguel@spotlab.ai
%K rapid diagnostic test
%K artificial intelligence
%K AI
%K telemedicine platform
%K COVID-19
%K rapid test
%K diagnostics
%K epidemiology
%K surveillance
%K automatic
%K automated
%K tracking
%D 2022
%7 30.12.2022
%9 Original Paper
%J JMIR Public Health Surveill
%G English
%X 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. 
%M 36265136
%R 10.2196/38533
%U https://publichealth.jmir.org/2022/12/e38533
%U https://doi.org/10.2196/38533
%U http://www.ncbi.nlm.nih.gov/pubmed/36265136