Simulation of visual acuity by personalizable neuro-physiological model of the human eye

Csilla Fülep,Illés Kovács,Gábor Erdei,Kinga Kránitz

doi:10.1038/s41598-019-44160-z

Abstract

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.

Highlights

We present a model of the whole visual train to estimate an individual’s visual acuity based on their eye’s physical properties
Where α0 denotes the visual angle in minutes of arc at the 50% recognition probability threshold by definition of the International Council of Ophthalmology (ICO) standard[1,2]
By examining the correct/incorrect identifications of several letters of a given size, it is possible to estimate the recognition probability from one line to the. Vision models use these data to calculate the psychometric function of vision by curve-fitting, from which the visual acuity value can be determined by thresholding[7,9]

Summary

Introduction

We present a model of the whole visual train to estimate an individual’s visual acuity based on their eye’s physical properties. Where α0 denotes the visual angle in minutes of arc at the 50% recognition probability threshold by definition of the International Council of Ophthalmology (ICO) standard[1,2] This measurement process is used to assess the entire visual system, as a result of which the acuity value depends on the optical parameters of the human eye, but is affected by factors such as retinal sampling, neural transfer, neural noise, and cortical recognition[6,7]. By examining the correct/incorrect identifications of several letters of a given size, it is possible to estimate the recognition probability from one line to the Vision models use these data to calculate the psychometric function of vision by curve-fitting, from which the visual acuity value can be determined by thresholding[7,9]. The main drawback of using artificial intelligence is that a huge number of samples are required during the training phase

Methods

Results

Conclusion