Ophthalmologists evaluate visual acuity tests by the number of correctly recognized optotypes (usually letters) in the different lines of an eye chart. This probability-based scoring results in significant statistical error that can only be decreased by the time-consuming analysis of a larger number of optotypes. In this paper, we present a new, more precise correlation-based scoring method that takes the degree of misidentification into consideration too, rather than the mere fact of it. According to our experimental results, this new method decreases the uncertainty error by 28% if using the same number of optotypes at a given letter size or requires half the optotype number to produce the same error as that of probability-based scoring.

We present a model of the whole visual train to estimate an individual’s visual acuity based on their eye’s physical properties. Our simulation takes into account the optics of the eye, neural transmission and noise, as well as the recognition process. Personalized input data are represented by the ocular wavefront aberration and pupil diameter, both either coming from in vivo measurements of a subject or being produced by optical design software using a schematic eye. This flexibility opens the door to a broad range of potential applications, such as objective visual acuity measurements and intraocular lens design. Our algorithm contains only two adjustable neural parameters: additive noise σ , and discrimination range δρ , with their values being experimentally calibrated by fitting the results of simulations to the outcome of real acuity tests performed on healthy young subjects with normal vision (visual acuity: 0…−0.3 logMAR range). It was established that by using fixed values of σ = 0.10 and δρ = 0.0025 for each person examined, the residual of the acuity simulations averaged over the calibration group reached its minimum at 0.045 logMAR.

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An amendment to this paper has been published and can be accessed via a link at the top of the paper.

Purpose Visual acuity tests are generally performed by showing eye charts to the subjects and registering their correct/incorrect identifications for the presented optotypes. We recently developed a correlation-based scoring method that significantly reduces the statistical error associated with relative letter legibility. In this paper, our purpose was to demonstrate the advantages and clinical utility of our scoring scheme compared to standard methods. Methods We developed a new computer-controlled measurement setup aligned with the ophthalmological standard. With this system, we presented the application of our correlation-based scoring in conventional clinical environment for 25 subjects and estimated the systematic error of the obtained acuity values. A separate experiment was performed by 14 additional subjects to reveal the test-retest variability of the new scoring method. Results The average systematic error relative to standard probability-based scoring is 0.01 logMAR over the examined subject group. Application of the correlation-based scheme when used in clinical environment with five letters per size decreases the repeatability error by ∼20% and increases diagnosis time by ∼10%. Conclusions The new scoring scheme is directly applicable in clinical practice providing unbiased results with improved repeatability compared to standard visual acuity measurements. It reduces test-retest variability by the same amount as if the number of letters was doubled in traditional tests. Translational Relevance Our new method is a promising alternative to conventional acuity tests in cases when high-precision measurements are required, for example evaluating implanted intraocular lenses, testing subjects with retinal diseases or cataract, and refractive surgery candidates.