Democratizing Optometric Care: A Vision-Based, Data-Driven Approach to Automatic Refractive Error Measurement for Vision Screening

Abstract

We present a vision-based, data-driven approach to identifying and measuring refractive errors in human subjects with low-cost, easily available equipment and no specialist training. Vision problems, such as refractive error (e.g. nearsightedness, astigmatism, etc) are common ocular problems, which, if uncorrected, may lead to serious visual impairment. The diagnosis of such defects conventionally requires expensive specialist equipment and trained personnel, which is a barrier in many parts of the developing world. Our approach aims to democratize optometric care by utilizing the computational power inherent in consumer-grade devices and the advances made possible by multimedia computing. We present results that show our system is able to match and outperform state-of-the-art medical devices under certain conditions.