Exploration of visual perception metrics as indicators for myopia screening

Area of Science:

• Ophthalmology and Vision Science
• Pediatric Health
• Biomedical Engineering

Background:

• Myopia, or nearsightedness, is a growing global health concern, particularly in children.
• Current large-scale myopia screening methods often lack comprehensive diagnostic capabilities.
• Integrating diverse indicators may improve the accuracy of identifying children at risk for myopia.

Purpose of the Study:

• To develop and validate a cost-effective, user-friendly myopia screening model.
• To integrate visual perception indicators with biometric indicators for enhanced myopia risk identification.
• To provide a feasible strategy for large-scale myopia screening in school settings.

Main Methods:

• 180 third-grade students (8-10 years old) in Shenzhen underwent myopia screening.
• Ocular biometrics (axial length/corneal radius ratio - AL/CR) and visual perception were assessed.
• Predictive models were created using AL/CR and visual perception metrics, with diagnostic efficacy evaluated via ROC curve analysis.

Main Results:

• Models integrating AL/CR (max) and visual perception indicators (PEP-DL (max), E 0.8 m) achieved a higher Area Under the Curve (AUC) of 0.794.
• A model solely based on AL/CR (max) had an AUC of 0.779, while a model using only visual perception indicators had an AUC of 0.709.
• Statistically significant differences were observed between emmetropia and screening myopia groups based on visual perception metrics.

Conclusions:

• Visual perception indicators and AL/CR (max) show comparable diagnostic effectiveness for myopia screening.
• Combining visual perception indicators with AL/CR (max) significantly improves myopia screening accuracy.
• This integrated approach enhances the precision and reliability of school-based myopia screening initiatives.