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基于人工智能的诊断和分级糖尿病视网膜病变的方法:综合性审查
Ibrahim Saleh1, Niveen Nasr El-Den2, Mohamed Elsharkawy3
1Department of Ophthalmology and Visual Sciences, University of Maryland School of Medicine, Baltimore, MD, USA.
Artificial intelligence in medicine
|July 24, 2025
概括
人工智能 (AI) 和深度学习 (DL) 对各种成像类型的自动糖尿病视网膜病变 (DR) 检测和分级充满希望. 本综述分析了91项研究,突出了DLL.
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:
- 糖尿病视网膜病变 (DR) 是全球主要的失明原因之一.
- 早期检测和准确的DR分级对于有效的治疗至关重要.
- 人工智能 (AI),计算机视觉,机器学习和深度学习 (DL) 为DR诊断提供了自动化解决方案.
研究的目的:
- 综合审查和评估基于人工智能的DR检测和分类方法.
- 分析使用 fundus 摄影,OCT,OCT血管学和光素血管学的研究.
- 为了比较23个公共DR数据集的特征.
主要方法:
- 对91项关于人工智能用于DR检测和分类的研究进行系统审查.
- 在基底摄影,OCT,OCT血管学和多式成像中分析AI方法.
- 对23个公共DR数据集的数据集特征进行比较.
主要成果:
- 深度学习 (DL) 方法通常在成像模式中优于传统的AI方法.
- 最常使用的图像是 fundus 图像 (81%),其次是 OCT 图像 (9%),OCT血管图像 (6%) 和多式联络图像 (2%).
- 人工智能主要用于多路分类 (62%),而不是二进制分类 (28%).
结论:
- 人工智能,特别是DL,显示了自动化DR检测和分级的巨大潜力.
- 未来的研究应该集中在可解释的人工智能,多模式数据集成和临床工作流集成上.
- 需要标准化的协议来将AI工具集成到医疗保健系统中,以改善DR管理.
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