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相关概念视频

Vision01:24

Vision

53.1K
Vision is the result of light being detected and transduced into neural signals by the retina of the eye. This information is then further analyzed and interpreted by the brain. First, light enters the front of the eye and is focused by the cornea and lens onto the retina—a thin sheet of neural tissue lining the back of the eye. Because of refraction through the convex lens of the eye, images are projected onto the retina upside-down and reversed.
53.1K
Visual System01:26

Visual System

561
Light enters the eye through the cornea, a transparent, dome-shaped surface covering the surface of the eyeball that helps to direct and focus incoming light. This light is then channeled toward the pupil, an adjustable opening whose size is controlled by the iris. The iris, a pigmented muscle, regulates the amount of light entering the eye by contracting or dilating the pupil, thereby ensuring optimal light levels for clear vision.
Once through the pupil, the light passes through the lens, a...
561

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相关实验视频

Updated: Jun 15, 2025

Computational Modeling of Retinal Neurons for Visual Prosthesis Research - Fundamental Approaches
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双向封闭循环单元网络模型可以生成具有可变数量的输入元素的未来视野.

Joohwang Lee1, Keunheung Park2, Hwayeong Kim1

  • 1Department of Ophthalmology, Pusan National University College of Medicine, Busan, Korea.

PloS one
|August 27, 2024
PubMed
概括

这项研究开发了一种双向封闭反复单位 (Bi-GRU) 模型,以预测未来的视觉现场测试. 该模型准确地预测视觉现场测试结果,有助于眼病管理.

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相关实验视频

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科学领域:

  • 眼科医生 眼科 眼科
  • 人工智能的人工智能
  • 医疗信息学 医疗信息学

背景情况:

  • 视觉现场测试对于诊断和监测玻璃眼病至关重要.
  • 预测未来的视野测试结果可以改善患者管理和治疗策略.
  • 深度学习模型为分析复杂的视觉场数据提供了潜力.

研究的目的:

  • 为了预测未来的视觉场测试,使用双向封闭循环单元 (Bi-GRU) 模型.
  • 根据输入视觉场测试和预测时间间隔的数量来评估Bi-GRU模型的性能.
  • 为了评估模型的准确性跨不同的青光眼严重程度.

主要方法:

  • 利用了来自23517只眼睛的185858个视野测试数据集进行训练,以及来自1053只眼睛的9459个测试数据集进行测试.
  • 开发了一个Bi-GRU架构,能够处理3到80个过去的视觉现场测试.
  • 预测的关键指标:平均偏差 (MD),模式标准偏差 (PSD),视野指数 (VFI) 和总偏差值 (TDV).

主要成果:

  • 对于MD,PSD,VFI和TDV的预测错误在可以接受的范围内 (例如,MD:1.20-1.68 dB,VFI:3.64-4.51%).
  • 预测误差随着预测时间间隔的延长而增加,尽管不是显著的.
  • 预测错误随着绿眼病的严重程度的恶化而显著增加.

结论:

  • 双GRU模型可靠地预测未来的视觉现场测试,仅使用前三次测试.
  • 这种以人工智能为驱动的方法显示出它在眼治疗中临床应用的前景.
  • 该模型的准确性受疾病严重程度和预测时间范围的影响.