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Related Experiment Video

Updated: Sep 9, 2025

$Subjective Refraction Test Using a Smartphone for Vision Screening$

Subjective Refraction Test Using a Smartphone for Vision Screening

Published on: October 18, 2024

Refractive error detection in smartphone images via convolutional neural network.

M K Michael Cheung¹, Zhongqi Yang², Xinwei Zhai³

¹School of Optometry, The Hong Kong Polytechnic University, Hong Kong, China.

International Journal of Medical Informatics

|August 30, 2025

Summary

This study introduces a novel method using convolutional neural network (CNN) models to estimate refractive error from smartphone images. The approach shows potential for accessible and efficient vision screening, particularly for myopia.

Keywords:

Computer-aided healthcare Mobile healthcare Photorefraction Vision screening

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Last Updated: Sep 9, 2025

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Smartphone Fundus Photography

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Published on: March 24, 2020

Area of Science:

Ophthalmology
Computer Vision
Artificial Intelligence

Background:

Refractive error is a common vision impairment that can lead to amblyopia.
Current vision screening methods are often inaccessible due to cost and expertise requirements.
Smartphone-based analysis offers a potential solution for democratizing vision screening.

Purpose of the Study:

To investigate the use of CNN-based models for accurate refractive error estimation.
To develop a method for screening visually significant myopic refractive error using smartphone images.
To assess the feasibility of an accessible and efficient vision screening tool.

Main Methods:

Utilized CNN models (MobileNetV2, EfficientNetB0, ResNet18) pre-trained on ImageNet.
Employed data augmentation to address data insufficiency.
Explored feature application for refractive error estimation and binary classification.

Main Results:

The MobileNetV2 model achieved a mean absolute error of approximately 0.616 for refractive error estimation.
Achieved around 85.3% accuracy in binary refractive error detection.
Demonstrated promising performance for both estimation and screening applications.

Conclusions:

This research is the first to apply CNN models for refractive error estimation and myopia screening.
The proposed method demonstrates potential as an accessible and efficient vision screening solution.
Smartphone-based CNN analysis could significantly improve global access to vision diagnostics.