Quantitative analysis of retinal vascular parameters changes in school-age children with refractive error using...

Area of Science:

• Ophthalmology
• Medical Imaging
• Artificial Intelligence in Healthcare

Background:

• Refractive errors, particularly myopia, are prevalent in school-aged children.
• Retinal vascular changes are associated with various ocular conditions.
• AI technology offers novel methods for quantitative analysis of retinal vasculature.

Purpose of the Study:

• To quantitatively analyze the relationship between spherical equivalent refraction (SER) and retinal vascular changes in children.
• To explore structural alterations in retinal vasculature using fundus photography and AI.
• To identify potential early predictors of myopia progression.

Main Methods:

• Retrospective case-control study of 226 eyes from 113 schoolchildren (aged 6-12).
• Categorization into low, intermediate, high myopia, and control groups based on SER.
• Quantitative analysis of retinal arteriovenous ratio (AVR) and vascular density using AI-powered fundus photography.

Main Results:

• High myopia group showed statistically significant decreases in AVR and vascular density compared to other groups.
• Strong negative correlation found between SER and ocular axial length (r=-0.874, p<0.001).
• Moderate positive correlation between SER and vascular density (r=0.527, p<0.001); moderate negative correlation between ocular axis and vascular density (r=-0.452, p<0.001).

Conclusions:

• Schoolchildren with high myopia exhibit reduced retinal vascular density and AVR.
• AVR and vascular density may serve as early predictive markers for myopia progression.
• AI-assisted fundus photography is a valuable tool for assessing retinal vascular health in pediatric myopia.