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开发和验证3D深度学习系统,用于对光学相干断层扫描图像的糖尿病黄斑的分类

Huishan Zhu¹, Jie Ji², Jian-Wei Lin¹

¹Joint Shantou International Eye Center of Shantou University and The Chinese University of Hong Kong, Shantou, China.

|May 31, 2025

概括

这项研究开发了一种自动化的深度学习系统,用于使用3D OCT图像对糖尿病黄斑 (DME) 进行分类. 该系统在以人口为基础的查中表现出高准确度,与专家可比.

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:

糖尿病黄斑 (DME) 是糖尿病患者视力丧失的主要原因.
准确及时对DME进行分类对于有效的治疗和管理至关重要.
当前的分类方法可能是耗时和主观的.

研究的目的:

开发和验证糖尿病黄斑 (DME) 的自动分类系统.
为了利用三维光学连贯断层扫描 (3-D OCT) 成像用于DME分类.
对人类专家进行深度学习 (DL) 算法的性能评估.

主要方法:

一项多中心研究,使用3D OCT设备的回顾性和横截面数据.
开发用于DME分类的3D卷积神经网络算法.
数据分为没有DME,非中心参与的DME和中心参与的DME (CI-DME).
与视网膜专家进行DL模型性能比较.

主要成果:

DL系统在DME分类方面取得了很高的准确性,非DME分类的AUROC达到0.990和CI-DME分类在外部验证数据上的0.881.
该系统的性能与视网膜专家的性能相当,科恩的卡帕值为0.85和0.75.
该模型在区分不同类别的DME时表现出强的表现.

结论:

开发的深度学习系统使用3D OCT图像准确地分类糖尿病黄斑.
该自动化系统显示了在基于人口的DME查中应用的潜力.
这项技术可以帮助有效和客观的DME诊断.

关键词:

糖尿病视网膜病变 - 糖尿病视网膜病变诊断成像诊断成像诊断成像经过多年的研究,我们发现Vetreoretinal.

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