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相关概念视频

Glaucoma: Overview01:25

Glaucoma: Overview

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Glaucoma is an eye condition characterized by increased intraocular pressure that damages the retina and optic nerve, leading to irreversible blindness if left untreated. The human eye has various components, including the cornea, iris, pupil, lens, and optic nerve. Aqueous humor is secreted by the epithelium of the ciliary body in the posterior chamber and flows through the trabecular meshwork and canal of Schlemm, maintaining normal intraocular pressure. The trabecular meshwork and the canal...
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Open Angle Glaucoma: Treatment01:27

Open Angle Glaucoma: Treatment

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In open-angle glaucoma, the iridocorneal angle remains open, but the trabecular meshwork becomes stiff, slowing down the outflow of aqueous humor. This causes a buildup of aqueous humor in the anterior chamber, leading to a sudden increase in intraocular pressure. The treatment for open-angle glaucoma focuses on reducing the elevated intraocular pressure by either decreasing the secretion of aqueous humor or increasing its outflow.
Drugs such as carbonic anhydrase inhibitors, α2- and...
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相关实验视频

Updated: Jan 10, 2026

Assessing Early Stage Open-Angle Glaucoma in Patients by Isolated-Check Visual Evoked Potential
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Assessing Early Stage Open-Angle Glaucoma in Patients by Isolated-Check Visual Evoked Potential

Published on: May 25, 2020

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一个视频定向的眼睛和基于rPPG的眼内压力检测方法.

Kun Zheng, Xuejia Zhen, Boxiang Hu

    IEEE journal of biomedical and health informatics
    |November 24, 2025
    PubMed
    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.

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  • 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.