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Updated: Jul 15, 2025

$Subjective Refraction Test Using a Smartphone for Vision Screening$

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Published on: October 18, 2024

Prediction of refractive error and its progression: a machine learning-based algorithm.

Maria J Barraza-Bernal¹, Arne Ohlendorf², Pablo Sanz Diez³

¹Technology and Innovation, Carl Zeiss Vision GmbH, Aalen, Germany maria-jose.barraza-bernal@zeiss.com.

BMJ Open Ophthalmology

|October 4, 2023

Summary

This summary is machine-generated.

A new machine learning algorithm accurately predicts myopia onset and progression in children. This tool aids eye care professionals in developing personalized myopia management strategies, crucial for addressing the rising prevalence of this refractive error.

Keywords:

optics and refraction public health

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Inducement and Evaluation of a Murine Model of Experimental Myopia

Inducement and Evaluation of a Murine Model of Experimental Myopia

Published on: January 22, 2019

Area of Science:

Ophthalmology and computational science.

Background:

Myopia prevalence is highest in young Southeast Asians and projected to increase.
Effective myopia management relies on accurate risk assessment for onset and progression.
Current risk assessment methods require improvement for personalized treatment strategies.

Purpose of the Study:

To develop and validate a machine learning algorithm for myopia risk assessment in children.
To provide an accessible tool for predicting refractive error development.

Main Methods:

Utilized population-based cross-sectional (12,780 children) and longitudinal (226 children) data from China.
Incorporated age, gender, biometric, and refractive parameters.
Developed a predictive model using a combination of support vector regression and Gaussian process regression.

Main Results:

The best-performing algorithm achieved a Pearson correlation coefficient of 0.77 between predicted and measured refractive data.
The model demonstrated a low bias of -0.05 D.
The limits of agreement were 0.85 D (95% CI: -0.91 to 0.80 D).

Conclusions:

The developed algorithm offers a reliable estimation of refractive development using accessible inputs.
This tool can guide eye care professionals in tailoring myopia management strategies for individual patients.
The findings support the use of machine learning in personalized ophthalmological care.