一个视频定向的眼睛和基于rPPG的眼内压力检测方法
IEEE journal of biomedical and health informatics
|November 24, 2025
概括
这项研究引入了一种新的基于视频的方法,使用远程光斑血学 (rPPG) 来检测和分类眼内压力 (IOP). IOP-Net模型显示了方便,无接触的IOP评估的潜力.
科学领域:
- 眼科医生 眼科 眼科
- 生物医学工程 生物医学工程
- 计算机视觉 计算机视觉
背景情况:
- 目前的眼内压力 (IOP) 测量依赖于不方便的接触方法.
- 需要非接触式,可访问的IOP监控解决方案.
- 眼科诊断可以从先进的成像技术中受益.
研究的目的:
- 开发和验证一种新的基于视频的方法来检测和分类眼内压力 (IOP).
- 在非接触性IOP评估中使用远程光电囊膜学 (rPPG).
- 创建一个能够从眼睛视频中区分正常和高IOP的AI模型.
主要方法:
- 开发了IOP-Net模型,一个有四层的卷积神经网络 (CNN).
- 从眼睛视频中的关键感兴趣区域 (ROI) 提取的血液体积脉冲 (BVP) 信号:瞳孔,虹膜和膜.
- 训练CNN模型使用提取的BVP信号进行IOP检测和分类.
主要成果:
- IOP-Net模型在EVIP-1数据集上实现了3.14 mmHg的根平均平方误差 (RMSE),在EVIP-2数据集上达到4.19 mmHg.
- 在地面真实性IOP超过30mmHg时,在对高IOP进行分类时表现出了80.25%的准确性.
- 通过基于视频的rPPG信号成功检测和分类IOP.
结论:
- 拟议的基于视频的rPPG方法为非接触性IOP检测和分类提供了一个有希望和潜在的方法.
- IOP-Net 证明了在远程 IOP 监控中广泛应用的可行性.
- 这种创新技术可以提高IOP评估的便利性和可访问性.
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