患近视的儿童和青少年的外周视网膜血管密度:深度学习评估
Yong Wang1,2,3, Tong Zhang1, Hangjia Zuo4,5,6
1Chongqing Medical University, Chongqing, People's Republic of China.
Ophthalmology and therapy
|February 14, 2026
概括
在儿童和青少年中,随着轴长度 (AL) 的增加,外周视网膜血管密度下降. 深度学习有效地检测这些亚临床血管变化,有助于早期高近视风险评估.
科学领域:
- 眼科医生 眼科 眼科
- 医疗成像医学成像
- 人工智能的人工智能
背景情况:
- 研究了轴长度 (AL) 对儿科患者周围视网膜血管结构的影响.
- 探索深度学习 (DL) 的潜力,以检测与近视相关的早期血管变化.
研究的目的:
- 为了确定轴长和外围视网膜血管密度之间的关系.
- 评估深度学习算法的有效性,以识别近视的亚临床血管变化.
主要方法:
- 使用非脑膜超广场成像来获取视网膜图像.
- 用户嵌套的U-Net和ResNet34深度学习模型用于血管细分和AL分类.
- 在外围视网膜领域的量化血管密度 (60-30°和100-60°).
主要成果:
- 分析了396名儿童和青少年的679只眼睛,这些眼睛来自正常,中等和高AL组.
- 观察到状视网膜的血管密度较高,下视网膜的血管密度较低.
- 周围血管密度显著下降,随着AL的增加 (正常>中等>高).
- 在使用深度学习的AL分类中实现了高精度 (AUC=0.9651),Grad-CAM突出显示了关键的血管区域.
结论:
- 轴长度增加与外周视网膜血管密度降低相关.
- 对超广场图像的深度学习分析可以检测出早期的亚临床血管变化.
- 这些发现表明DL在早期检测高近视风险方面可能发挥作用.
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