RESUMO
BackgroundThe worldwide surge in coronavirus cases has led to the COVID-19 testing demand surge. Rapid, accurate, and cost-effective COVID-19 screening tests working at a population level are in imperative demand globally. MethodsBased on the eye symptoms of COVID-19, we developed and tested a COVID-19 rapid prescreening model using the eye-region images captured in China and Spain with cellphone cameras. The convolutional neural networks (CNNs)-based model was trained on these eye images to complete binary classification task of identifying the COVID-19 cases. The performance was measured using area under receiver-operating-characteristic curve (AUC), sensitivity, specificity, accuracy, and F1. The application programming interface was open access. FindingsThe multicenter study included 2436 pictures corresponding to 657 subjects (155 COVID-19 infection, 23{middle dot}6%) in development dataset (train and validation) and 2138 pictures corresponding to 478 subjects (64 COVID-19 infections, 13{middle dot}4%) in test dataset. The image-level performance of COVID-19 prescreening model in the China-Spain multicenter study achieved an AUC of 0{middle dot}913 (95% CI, 0{middle dot}898-0{middle dot}927), with a sensitivity of 0{middle dot}695 (95% CI, 0{middle dot}643-0{middle dot}748), a specificity of 0{middle dot}904 (95% CI, 0{middle dot}891-0{middle dot}919), an accuracy of 0{middle dot}875(0{middle dot}861-0{middle dot}889), and a F1 of 0{middle dot}611(0{middle dot}568-0{middle dot}655). InterpretationThe CNN-based model for COVID-19 rapid prescreening has reliable specificity and sensitivity. This system provides a low-cost, fully self-performed, non-invasive, real-time feedback solution for continuous surveillance and large-scale rapid prescreening for COVID-19. FundingThis project is supported by Aimomics (Shanghai) Intelligent
