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相关概念视频

Focusing of Light in the Eye01:16

Focusing of Light in the Eye

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Light rays enter the eye through the cornea, a transparent dome-shaped tissue that is the eye's outermost layer. The cornea bends or refracts, light rays traveling to the pupil. The shape of the cornea determines how much of the light is bent and whether the image will be focused correctly on the retina at the back of the eye. Once the light has passed through both refraction layers, it converges into a single focal point onto a small area. This is where photoreceptors start transforming...
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相关实验视频

Updated: Jul 15, 2025

Subjective Refraction Test Using a Smartphone for Vision Screening
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预测折射误差及其进展:基于机器学习的算法.

Maria J Barraza-Bernal1, Arne Ohlendorf2, Pablo Sanz Diez3

  • 1Technology and Innovation, Carl Zeiss Vision GmbH, Aalen, Germany maria-jose.barraza-bernal@zeiss.com.

BMJ open ophthalmology
|October 4, 2023
PubMed
概括
此摘要是机器生成的。

一个新的机器学习算法准确地预测了儿童近视的发病和进展. 该工具帮助眼科专业人员制定个性化的近视管理策略,这对于解决这种折射误差日益增长的患病率至关重要.

关键词:
光学和折射的光学和折射.公共卫生公共卫生.

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科学领域:

  • 眼科和计算科学.

背景情况:

  • 近视的患病率在年轻的东南亚人中最高,预计将增加.
  • 有效的近视管理依赖于对发病和进展的准确风险评估.
  • 目前的风险评估方法需要改进,以实现个性化治疗策略.

研究的目的:

  • 开发和验证用于儿童近视风险评估的机器学习算法.
  • 为预测折射误差发展提供一个可访问的工具.

主要方法:

  • 利用来自中国的基于人口的截面 (12,780名儿童) 和纵向 (226名儿童) 数据.
  • 集成的年龄,性别,生物识别和折射参数.
  • 开发了一个预测模型,使用支向量回归和高斯过程回归的组合.

主要成果:

  • 性能最好的算法在预测和测量的折射数据之间实现了0.77的皮尔森相关系数.
  • 该模型显示低偏差为-0.05 D.
  • 协议的极限为0.85 D (95% CI: -0.91 至 0.80 D).

结论:

  • 开发的算法使用可访问的输入提供了可靠的折射发展估计.
  • 该工具可以指导眼科专业人员为个别患者量身定制近视管理策略.
  • 这些发现支持在个性化眼科护理中使用机器学习.