Predicting subjective refraction with dynamic retinal image quality analysis

Ahhyun Stephanie Nam,Eduardo Lage,Andrea Gil,Carlos S Hernández,Shivang R Dave,Varshini Varadaraj,Nicholas J Durr,Daryl Lim

doi:10.1038/s41598-022-07786-0

Abstract

The aim of this work is to evaluate the performance of a novel algorithm that combines dynamic wavefront aberrometry data and descriptors of the retinal image quality from objective autorefractor measurements to predict subjective refraction. We conducted a retrospective study of the prediction accuracy and precision of the novel algorithm compared to standard search-based retinal image quality optimization algorithms. Dynamic measurements from 34 adult patients were taken with a handheld wavefront autorefractor and static data was obtained with a high-end desktop wavefront aberrometer. The search-based algorithms did not significantly improve the results of the desktop system, while the dynamic approach was able to simultaneously reduce the standard deviation (up to a 15% for reduction of spherical equivalent power) and the mean bias error of the predictions (up to 80% reduction of spherical equivalent power) for the handheld aberrometer. These results suggest that dynamic retinal image analysis can substantially improve the accuracy and precision of the portable wavefront autorefractor relative to subjective refraction.

Highlights

Those image quality metrics (IQM), which can be based on a variety of optical25, neural[24], or imaging-related[26,27] parameters, have been used to optimize the agreement between objective and subjective refraction with different degrees of success[21,22,25,27]
Precision of each IQM was defined as twice the standard deviation of the differences between the predicted refraction and subjective refraction for M, J0, and J45; this corresponds to the 95% limits of agreement of Bland–Altman analysis
Image quality metrics analyze different characteristics of light in the computed retinal image and are important factors when considering visual acuity. They have been widely used in search-based optimization procedures[21,27,28]. This method has been replicated in the present study using the wavefront information provided by a desktop wavefront aberrometer

Summary

Introduction

keratoconus[17], neural adaptation to refractive c orrection[1,18] or high-order aberrations and their variations with pupil size[19] amongst others. It has been demonstrated that wavefront information can be analyzed to estimate objective quality metrics that describe the optical and perceptual image quality of a s ubject[21,24] Those image quality metrics (IQM), which can be based on a variety of optical25, neural[24], or imaging-related[26,27] parameters, have been used to optimize the agreement between objective and subjective refraction with different degrees of success[21,22,25,27]. When using these metrics, the refraction optimization process consists of a search in a synthetically generated 3-dimensional space to find sphere, cylinder, and axis values of a correcting lens that optimizes a certain IQM when applied to a static wavefront aberration measurement of a subject. We propose a novel algorithm capable of using the dynamic aberrometry information and evaluate its ability to simultaneously improve the precision and accuracy of autorefraction compared to subjective refraction

Objectives

Methods

Results

Conclusion