Background: To develop and assess usability of a smartphone-based visual acuity (VA) test with an automatic distance calibration (ADC) function, the iOS version of WHOeyes. Methods: The WHOeyes was an upgraded version with a distinct feature of ADC of an existing validated VA testing APP called V@home. Three groups of Chinese participants with different ages (≤20, 20-40, >40 years) were recruited for distance and near VA testing using both an Early Treatment Diabetic Retinopathy Study (ETDRS) chart and the WHOeyes. The ADC function would determine the testing distance. Infrared rangefinder was used to determine the testing distance for the ETDRS, and actual testing distance for the WHOeyes. A questionnaire-based interview was administered to assess satisfaction. Results: The actual testing distance determined by the WHOeyes ADC showed an overall good agreement with the desired testing distance in all three age groups (p > 0.50). Regarding the distance and near VA testing, the accuracy of WHOeyes was equivalent to ETDRS. The mean difference between the WHOeyes and ETDRS ranged from -0.084 to 0.012 logMAR, and the quadratic weighted kappa (QWK) values were greater than 0.75 across all groups. The test-retest reliability of WHOeyes was high for both near and distance VA, with a mean difference ranging from -0.040 to 0.004 logMAR and QWK all greater than 0.85. The questionnaire revealed an excellent user experience and acceptance of WHOeyes. Conclusions: WHOeyes could provide accurate measurement of the testing distance as well as the distance and near VA when compared to the gold standard ETDRS chart. Keywords: smartphone-based; visual acuity test; WHOeyes, V@home; ETDRS;

Abstract: Diabetic retinopathy (DR) remains a leading cause of irreversible vision loss in adult populations around the globe. Despite growing evidence of the effectiveness of routine assessments and early intervention, DR screening strategies are not widely implemented largely due to an inadequate availability of resources to cope with the growing burden of diabetes. Advances in technology in the field of DR screening are clearly warranted and the recent emergence of deep learning-based artificial intelligence (AI) grading of retinal pathology offers significant potential benefits including an increased efficiency, accessibility and affordability of screening programmes.