  Background: The 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.
  Methods: Based 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.
  Findings: The multicenter study included 2436 pictures corresponding to 657
subjects (155 COVID-19 infection, 23.6%) in development dataset (train and
validation) and 2138 pictures corresponding to 478 subjects (64 COVID-19
infections, 13.4%) in test dataset. The image-level performance of COVID-19
prescreening model in the China-Spain multicenter study achieved an AUC of
0.913 (95% CI, 0.898-0.927), with a sensitivity of 0.695 (95% CI, 0.643-0.748),
a specificity of 0.904 (95% CI, 0.891 -0.919), an accuracy of
0.875(0.861-0.889), and a F1 of 0.611(0.568-0.655).
  Interpretation: The 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.
  Funding: This project is supported by Aimomics (Shanghai) Intelligent
