近视网:基于深度学习的近视发病和进展的直接识别
Zengshuo Wang1,2, Haohan Zou1,3, Yin Guo4
1Nankai University Eye Institute, Nankai University, Tianjin, China.
Translational vision science & technology
|August 27, 2025
概括
一个新的深度神经网络,Myopic-Net,通过视网膜图像准确地检测近视的开始和进展. 这种人工智能工具显示出方便,个性化的近视监测的巨大潜力.
科学领域:
- 眼科 眼科
- 医学成像
- 人工智能
背景情况:
- 在临床应用中,近视发病和进展 (MOP) 的监测至关重要.
- 检测MOP依赖于分析 fundus视网膜图像中的解剖变化.
- 深度神经网络 (DNN) 为自动化分析提供了一个有前途的方法.
研究的目的:
- 评估DNN在识别和监测MOP方面的表现.
- 开发和验证用于从视网膜图像检测MOP的DNN模型.
主要方法:
- 一个名为Myopic-Net的DNN是使用6344个 fundus图像对 (3964个没有MOP,2380个有MOP) 来开发的.
- 在内部和外部测试组中使用准确性,精度,回忆,特异性和F1分数来评估模型的性能.
- 使用深度网络可视化来理解预测驱动因素.
主要成果:
- 在内部测试组中,Myopic- Net的准确率达到87. 3%,超过了人类眼科医生 (66. 1%和73. 5%).
- 该模型在独立的外部测试组中保持了84.1%的准确性.
- 发现的关键预测因素是光盘和周围区域的解剖变化.
结论:
- 通过分析光盘变化,Myopic-Net有效地从 fundus 图像中识别出 MOP.
- 该模型具有高准确性,可靠性和概括性.
- DNN显示了使用底部图像分析监测和诊断MOP的巨大潜力.
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