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.