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The Coronavirus disease 2019 (COVID-19) has affected several million people.With the outbreak of the epidemic, many researchers are devoting themselves tothe COVID-19 screening system. The standard practices for rapid risk screeningof COVID-19 are the CT imaging or RT-PCR (real-time polymerase chain reaction).However, these methods demand professional efforts of the acquisition of CTimages and saliva samples, a certain amount of waiting time, and mostimportantly prohibitive examination fee in some countries. Recently, someliteratures have shown that the COVID-19 patients usually accompanied by ocularmanifestations consistent with the conjunctivitis, including conjunctivalhyperemia, chemosis, epiphora, or increased secretions. After more than fourmonths study, we found that the confirmed cases of COVID-19 present theconsistent ocular pathological symbols; and we propose a new screening methodof analyzing the eye-region images, captured by common CCD and CMOS cameras,could reliably make a rapid risk screening of COVID-19 with very high accuracy.We believe a system implementing such an algorithm should assist the triagemanagement or the clinical diagnosis. To further evaluate our algorithm andapproved by the Ethics Committee of Shanghai public health clinic center ofFudan University, we conduct a study of analyzing the eye-region images of 303patients (104 COVID-19, 131 pulmonary, and 68 ocular patients), as well as 136healthy people. Remarkably, our results of COVID-19 patients in testing setconsistently present similar ocular pathological symbols; and very high testingresults have been achieved in terms of sensitivity and specificity. We hopethis study can be inspiring and helpful for encouraging more researches in thistopic.

A New Screening Method for COVID-19 based on Ocular Feature Recognition by Machine Learning Tools