Predicting subjective refraction with dynamic retinal image quality analysis

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

Research output: Contribution to journalArticlepeer-review

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.

Original languageEnglish (US)
Article number3714
JournalScientific reports
Volume12
Issue number1
DOIs
StatePublished - Dec 2022

ASJC Scopus subject areas

  • General

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