Performance of an artificial intelligence-based smartphone app for guided reading of SARS-CoV-2 lateral-flow immunoassays

Objectives To evaluate an artificial intelligence-based smartphone application 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 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 92 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 platform can serve as a real time epidemiological tracking tool and facilitate reporting of positive RDTs to relevant health authorities.