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Introduction <p>This study investigated the effect of axial length (AL) on peripheral retinal vessel density in children and adolescents and assessed whether deep learning can detect early vascular changes in myopia.</p> Methods <p>Non-mydriatic ultra-widefield imaging was used to capture retinal images. Deep learning models based on Nested U-Net and ResNet34 segmented the vasculature, quantified vessel density in 60–30° and 100–60° fields, and classified AL from fundus images.</p> Results <p>A total of 679 eyes from 396 children and adolescents were analyzed. Participants were categorized into normal (22.96 ± 0.65&#xa0;mm), medium (24.69 ± 0.50&#xa0;mm), and high (27.32 ± 0.80&#xa0;mm) AL groups. Across both 60–30° and 100–60° fields, the temporal retina displayed higher vessel density, while the inferior retina showed lower density. The normal AL group had significantly higher density than the medium AL group (<i>P</i> &lt; 0.05), which surpassed the high AL group (<i>P</i> &lt; 0.05). In the 60–30° temporal region, vessel density decreased from 7.15 ± 1.17% (normal) to 6.70 ± 1.27% (medium) and 6.16 ± 1.82% (high). Deep learning classification achieved an AUC of 0.9651, with Grad-CAM highlighting the inferotemporal vasculature.</p> Conclusions <p>As AL increases, peripheral vessel density diminishes. This pattern may suggest a potential prioritization of blood flow to the macular region, although longitudinal studies are required to confirm this hypothesis. These findings suggest that deep learning analysis of ultra-widefield images can reveal subclinical vascular changes, offering a potential tool for early detection of high myopia risk.</p>

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Peripheral Retinal Vessel Density in Children and Adolescents with Myopia: A Deep Learning Assessment

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年2月14日

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  • Yong Wang,
  • Tong Zhang,
  • Hangjia Zuo,
  • Yanlin Yang,
  • Yongguo Xiang,
  • Kexin Sun,
  • Xin Yang,
  • Jiuyi Xia,
  • Fanfan Huang,
  • Shenglan Yi,
  • Shijie Zheng,
  • Ke Hu,
  • Wenjuan Wan

摘要

Introduction

This study investigated the effect of axial length (AL) on peripheral retinal vessel density in children and adolescents and assessed whether deep learning can detect early vascular changes in myopia.

Methods

Non-mydriatic ultra-widefield imaging was used to capture retinal images. Deep learning models based on Nested U-Net and ResNet34 segmented the vasculature, quantified vessel density in 60–30° and 100–60° fields, and classified AL from fundus images.

Results

A total of 679 eyes from 396 children and adolescents were analyzed. Participants were categorized into normal (22.96 ± 0.65 mm), medium (24.69 ± 0.50 mm), and high (27.32 ± 0.80 mm) AL groups. Across both 60–30° and 100–60° fields, the temporal retina displayed higher vessel density, while the inferior retina showed lower density. The normal AL group had significantly higher density than the medium AL group (P < 0.05), which surpassed the high AL group (P < 0.05). In the 60–30° temporal region, vessel density decreased from 7.15 ± 1.17% (normal) to 6.70 ± 1.27% (medium) and 6.16 ± 1.82% (high). Deep learning classification achieved an AUC of 0.9651, with Grad-CAM highlighting the inferotemporal vasculature.

Conclusions

As AL increases, peripheral vessel density diminishes. This pattern may suggest a potential prioritization of blood flow to the macular region, although longitudinal studies are required to confirm this hypothesis. These findings suggest that deep learning analysis of ultra-widefield images can reveal subclinical vascular changes, offering a potential tool for early detection of high myopia risk.