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使用基于深度学习自编码器的感兴趣区域在近视眼中检测青光眼

Christopher Bowd¹, Akram Belghith¹, Mark Christopher¹

¹Hamilton Glaucoma Center and Division of Ophthalmology Informatics and Data Science, Shiley Eye Institute, Viterbi Family Department of Ophthalmology, University of California (UC) San Diego, La Jolla, CA, United States.

Frontiers in ophthalmology

|August 20, 2025

概括

一个双自编码深度学习模型使用光学连贯断层扫描 (OCT) 纹理图像准确地检测了近视眼中的青光眼. 这种先进的模型优于传统的方法,

科学领域:

眼科眼科
医学成像
人工智能

背景情况:

在近视眼中诊断眼症具有挑战性.
目前的诊断方法依赖于视网膜神经纤维层 (RNFL) 厚度等指标.
光学连贯断层扫描 (OCT) 提供了详细的眼睛成像.

研究的目的:

评估深度学习自编码模型用于近视眼中的青光眼检测.
通过OCT纹理图像利用感兴趣的区域 (ROI).
将模型的准确性与传统方法进行比较.

主要方法:

一项横截面研究包括了315名参与者的453只眼睛 (健康和眼).
用扫描源OCT (SS-OCT) 成像来构建纹理面部图像.
四种方法进行了比较:RNFL厚度,纹理接口,单自编码器和双自编码器模型.
使用接收器操作曲线下的面积 (AUROC) 和精确回忆曲线下的面积 (AUPRC) 评估了诊断准确性.

主要成果:

双自编码器模型实现了最高的AUROC (0.92) 和AUPRC (0.86).
这种模型显著优于单个自动编码器,RNFL厚度和纹理接口模型 (p < 0.05).
在RNFL厚度和纹理面部测量之间没有发现显著差异 (p = 0.47).

结论:

双自编码模型在近视眼中显示出更高的诊断准确性.
这种基于ROI的重建错误的深度学习方法是传统指标的强大替代方案.
这些发现表明,使用先进的人工智能模型增强了青光眼的分类潜力.

关键词:

人工智能进行分类深度学习诊断情况青光眼眼睛近视光学连贯断层扫描

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