Abstract
PURPOSE: Visual acuity, measured by resolution of optotypes on a standard eye chart, is a critical clinical test for function of the visual system in humans. Behavioral tests in animals can be used to estimate visual acuity. However, such tests may be limited in the study of mutants or after synthetic vision restoration techniques. Because the total response of the retina to a visual scene is encoded in spiking patterns of retinal ganglion cells, it should be possible to estimate visual acuity in vitro from the retina by analyzing retinal ganglion cell output in response to test stimuli. METHODS: We created a method, EyeCandy, that combines a visual stimulus-generating engine with analysis of multielectrode array retinal recordings via a machine learning approach to measure murine retinal acuity in vitro. Visual stimuli included static checkerboards, drifting gratings, and letter optotypes. RESULTS: In retinas from wild-type C57Bl/6 mice, retinal acuity measurement for a drifting grating was 0.4 cycles per degree. In contrast, retinas from adult rd1 mice with outer retinal degeneration showed no detectable acuity. A comparison of acuities among different regions of the retina revealed substantial variation, with the inferior-nasal quadrant having highest RA. Letter classification accuracy of a projected Early Treatment Diabetic Retinopathy eye chart reached 99% accuracy for logMAR 3.0 letters. EyeCandy measured a restored RA of 0.05 and 0.08 cycles per degree for static and dynamic stimuli respectively from the retina of the rd1 mouse treated with the azobenzene photoswitch BENAQ. CONCLUSIONS: Machine learning may be used to estimate retinal acuity. TRANSLATIONAL RELEVANCE: The use of ex vivo retinal acuity measurement may allow determination of effects of mutations, drugs, injury, or other manipulations on retinal visual function.