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相关概念视频

Glaucoma: Overview01:25

Glaucoma: Overview

623
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...
623
Open Angle Glaucoma: Treatment01:27

Open Angle Glaucoma: Treatment

481
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...
481

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相关实验视频

Updated: Jul 23, 2025

Assessing Early Stage Open-Angle Glaucoma in Patients by Isolated-Check Visual Evoked Potential
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基于使用深度学习方法的视网膜底部图像的自动化玻璃眼查和诊断:全面的审查

Mohammad J M Zedan1,2, Mohd Asyraf Zulkifley1, Ahmad Asrul Ibrahim1

  • 1Department of Electrical, Electronic and Systems Engineering, Faculty of Engineering and Built Environment, Universiti Kebangsaan Malaysia, Bangi 43600, Selangor, Malaysia.

Diagnostics (Basel, Switzerland)
|July 14, 2023
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概括

深度学习算法显示出从视网膜图像中早期发现青光眼的前景. 这一系统性审查分析了52项研究,突出了AI.

关键词:
杯盘比 (CDR) 是指杯盘的比率.深度学习是一种深度学习.青光眼的查和诊断 青光眼的查和诊断视神经的头部 (ONH)视网膜疾病 视网膜疾病视网膜底部图像 视网膜底部图像

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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:

  • 玻璃眼睛瘤是一种慢性眼睛疾病,由于视神经受损导致不可逆转的视力丧失,通常是由眼内压力升高引起的.
  • 早期诊断和治疗对于治疗眼和预防视力损伤至关重要.
  • 目前的诊断方法依赖于熟练的眼科医生解释视网膜图像,这可能是主观的和耗时的.

研究的目的:

  • 系统地审查和分析52项关于用于青光眼查和诊断的深度学习算法的最新研究.
  • 评估数据集,性能指标和用于基于人工智能的青光眼检测方法.
  • 为了比较各种深度学习方法在分析视网膜底部图像中的优缺点.

主要方法:

  • 52篇最先进的研究文章的系统文献综述.
  • 对以图像预处理,定位,分类和视网膜结构细分为重点的算法的分析.
  • 评估数据集,性能指标和深度学习模型中用于青光眼诊断的成像模式.

主要成果:

  • 深度学习算法显示出从视网膜底图像准确查和诊断眼的巨大潜力.
  • 审查确定了用于开发这些AI诊断工具的各种方法和数据集.
  • 对比分析突出了不同深度学习技术的优点和局限性,以检测青光眼.

结论:

  • 使用深度学习算法自动化青光眼诊断为提高诊断准确性和效率提供了一个有希望的途径.
  • 由人工智能驱动的系统可以帮助眼科医生,可能导致更早的检测和更好的患者结果.
  • 进一步的研究和验证对于将这些先进的诊断工具纳入临床实践至关重要.