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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: Jun 8, 2025

Assessing Early Stage Open-Angle Glaucoma in Patients by Isolated-Check Visual Evoked Potential
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Published on: May 25, 2020

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使用光学一致性断层扫描检测玻璃眼恶化 导出视野估计 视野估计

Alex T Pham, Chris Bradley, Kaihua Hou

    medRxiv : the preprint server for health sciences
    |November 1, 2024
    PubMed
    概括

    机器学习模型将光学连贯性断层扫描 (OCT) 数据转换为视野 (VF) 平均偏差 (MD) 估计尚未足够准确以检测纵向VF进展. 目前的模型要求平均绝对误差 (MAE) 为1dB或更小,才能具有临床价值.

    科学领域:

    • 眼科医生 眼科 眼科
    • 医疗成像医学成像
    • 机器学习 机器学习

    背景情况:

    • 截面光学连贯性断层扫描 (OCT) 数据已被用于估计视野 (VF) 平均偏差 (MD).
    • 这些模型在检测纵向VF进展中的实用性仍然不确定.

    研究的目的:

    • 开发和评估一种机器学习 (ML) 模型,用于将OCT数据转换为MD估计.
    • 评估模型在检测纵向VF恶化的能力.

    主要方法:

    • 训练了一种ML模型,使用70,575个配对的OCT/VF数据集将OCT转换为VF-MD.
    • 评估了模型在4044只眼睛的进展数据集上的表现,其中至少有5个配对的OCT/VF.
    • 通过替换/补充VF-MD与OCT衍生的MD (OCT-MD) 来计算MD斜率,并将曲线下的面积 (AUC) 与地面真相进行比较.

    主要成果:

    • 据CT-MD估计,平均绝对误差 (MAE) 为1.62dB.
    • 部分OCT-MD替代的MD斜率的AUC明显比VF-MD斜率差.
    • 补充VF-MD与OCT-MD并没有改善AUC;为了统计相似性,需要MAE≤1.00dB.

    结论:

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    • 目前的ML模型将OCT转换为VF-MD,即使精度提高 (MAE:1.62dB),在检测基于趋势的进展方面也低于VF-MD.
    • 为了在检测VF恶化的临床实用性,OCT-to-VF-MD模型需要MAE的预测误差≤1dB.