基于多模式的非接触式高眼内压检测方法
Zibo Lan1, Ying Hu2, Shuang Yang1
1School of Information Science and Engineering, Shenyang University of Technology, Shenyang 110870, China.
Sensors (Basel, Switzerland)
|July 30, 2025
概括
这项研究引入了一种新的深度学习方法,使用眼睛图像和角膜生物力学来检测高眼内压 (IOP). 这种方法可以实现高精度的青光眼查,提供一种非接触的替代方案.
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:
- 玻璃眼,不可逆转失明的主要原因,需要精确的眼内压力 (IOP) 监测.
- 传统的IOP测量容易导致角膜生物力学变化,影响诊断可靠性.
研究的目的:
- 通过整合 Scheimpflug 图像和角膜生物力学数据,开发一种非接触式,基于深度学习的方法来检测升高的 IOP.
- 为了提高高IOP预测和玻璃眼查的准确性.
主要方法:
- 一个多模式的深度学习框架,利用CycleGAN进行数据增强,Swin Transformer进行视觉特征提取,以及Kolmogorov-Arnold网络 (KAN) 进行数据融合.
- 将Scheimpflug成像与临床参数进行整合,以进行全面的数据分析.
- 数据集的增强,以解决数据稀缺和阶级不平衡的问题.
主要成果:
- 拟议的模型在现实数据集上实现了0.91的诊断准确度,超过了传统方法.
- Grad-CAM可视化突出了与IOP波动相关的关键解剖特征,包括角膜厚度和前腔深度.
结论:
- 这项研究表明了深度学习方法的有效性,该方法结合了眼睛成像和生物力学,以准确评估IOP.
- 这些发现强调了角膜结构在IOP调节中的重要性,并表明了非侵入性,生物力学信息的IOP查的潜力.
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