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Related Experiment Video

Updated: Jan 8, 2026

$Comparison of Agreement and Accuracy using Binocular Wavefront Optometer with Autorefractor and Phoropter$

Comparison of Agreement and Accuracy using Binocular Wavefront Optometer with Autorefractor and Phoropter

Published on: September 16, 2025

Fast and accurate visual acuity prediction based on optical aberrations and machine learning.

A Sierra¹, I Baoud-Ould-Haddi², S Fernández-Núñez³

¹Departamento de Óptica, Facultad de Ciencias Físicas, Universidad Complutense de Madrid, Plaza de Ciencias 1, 28040, Madrid, Spain. aguesier@ucm.es.

Scientific Reports

|December 19, 2025

Summary

This summary is machine-generated.

We developed machine learning models to predict visual acuity (VA). LSBoost regression trees achieved the highest accuracy, while XGBoost offered faster computation, making them suitable for visual compensation design.

Keywords:

Amplitude of accommodation Neural networks Regression trees Visual acuity (VA)XGBoost Zernike coefficients

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Area of Science:

Ophthalmology
Computer Science
Machine Learning

Background:

Visual acuity (VA) prediction is crucial for eye care.
Current methods may lack efficiency or accuracy.
Machine learning offers potential for improved VA prediction.

Purpose of the Study:

To develop and evaluate machine learning models for predicting visual acuity.
To compare the performance of regression trees (LSBoost, XGBoost) and a neural network for VA prediction.

Main Methods:

Three machine learning models were proposed: LSBoost, XGBoost, and a neural network.
Data included Zernike coefficients, accommodation amplitudes, age, and VA from 135 subjects.
Models simulated clinical optotype recognition for VA estimation.

Main Results:

LSBoost demonstrated superior prediction accuracy, particularly with accommodation data.
XGBoost provided faster computation times, beneficial for large datasets.
The neural network showed high optotype recognition but lower VA prediction accuracy.

Conclusions:

Regression tree models, especially LSBoost, are highly suitable for VA prediction using tabulated clinical data.
LSBoost excels in accuracy, while XGBoost offers computational efficiency.
Machine learning provides a promising avenue for advancing visual acuity assessment.