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基于不同眼睛成像模式的深度学习模型的多重比较,用于视觉显著的白内障检测.

Jocelyn Hui Lin Goh1, Xiaofeng Lei2, Miao-Li Chee1

  • 1Singapore Eye Research Institute, Singapore National Eye Centre, Singapore.

Ophthalmology science
|August 12, 2025
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概括

一种新的视网膜成像深度学习模型在检测视觉显著白内障 (VSC) 中表现出优异的性能,与其他眼部成像方法相比. 这一进步为在例行糖尿病视网膜病变检查期间进行机会性白内障查提供了潜力.

关键词:
人工智能的人工智能是人工智能.发现白内障的检测仪深度学习是一种深度学习.眼睛成像 眼睛成像视网膜成像 视网膜成像

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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:

  • 与年龄相关的白内障是全球视力障碍的主要原因.
  • 深度学习 (DL) 算法正在开发,用于使用各种成像技术进行自动化白内障分析.
  • 由于缺乏标准化数据集,不同眼睛成像模式的DL模型的比较性能尚未得到充分确立.

研究的目的:

  • 评估和比较深度学习 (DL) 算法在不同眼睛成像模式中检测视觉显著白内障 (VSC) 的性能.
  • 评估单模式DL模型 (视网膜,裂纹光束,扩散前段) 和整体模型.

主要方法:

  • 开发了三种单一模式DL模型和四种集体模型用于VSC检测.
  • 使用大型数据集 (新加坡马来眼睛研究,SINDI,SCES) 进行培训和外部验证.
  • 基于威斯康星州修改后的白内障分级系统和最佳校正视力敏度<20/60.的VSC定义.

主要成果:

  • 视网膜成像DL模型在内部测试 (97.0%) 和外部测试中获得了最高的接收器操作特征曲线 (AUC) 下的面积.
  • 在内部测试中,视网膜模型的表现优于切割束 (93.4%) 和扩散前段 (94.4%).
  • 视网膜模型在非水性视网膜照片中显示出合理的性能 (AUC,89.8%).

结论:

  • 视网膜成像DL模型是检测VSC的一个有希望的工具,超过了其他测试的模式.
  • 视网膜摄影在糖尿病视网膜病变查中的常规使用,使得具有成本效益的机会性白内障查成为可能.
  • 这种方法可以将白内障检测整合到现有的眼睛查程序中.