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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: Jun 17, 2025

Subjective Refraction Test Using a Smartphone for Vision Screening
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Subjective Refraction Test Using a Smartphone for Vision Screening

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基于眼镜图像的深度学习用于预测折射误差: 一个系统的审查.

Samantha Min Er Yew1,2, Yibing Chen3, Jocelyn Hui Lin Goh4

  • 1Department of Ophthalmology, Yong Loo Lin School of Medicine, National University of Singapore, Singapore.

Advances in ophthalmology practice and research
|August 8, 2024
PubMed
概括
此摘要是机器生成的。

深度学习模型在使用眼睛图像检测折射误差方面表现有前途. 视网膜和外部眼睛的照片显示了近视预测和持续折射误差评估的特别强性能.

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Binocular Dynamic Visual Acuity in Eyeglass-Corrected Myopic Patients
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Last Updated: Jun 17, 2025

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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
  • 医疗成像医学成像

背景情况:

  • 未纠正的折射误差是全球视力障碍的重要原因之一.
  • 深度学习 (DL) 为眼科诊断提供了先进的解决方案.
  • 需要对用于反射误差检测的DL模型进行系统审查.

研究的目的:

  • 系统地审查和评估基于眼镜图像的深度学习模型,用于折射误差预测.
  • 总结DL模型在不同成像模式中的性能.

主要方法:

  • 系统地搜索PubMed,Scopus和Web of Science数据库,直到2023年6月.
  • 包括报告折射误差结果的研究,不论出版年份.
  • 根据PRISMA指南,提取和评估连续和分类结果,成像模式,DL模型和性能指标.

主要成果:

  • 九项研究被分为基于视网膜照片的 (5),基于OCT的 (1) 和基于外部眼睛照片的 (3).
  • 基于视网膜照片的模型显示了近视预测 (AUC 0.91-0.98) 和连续预测 (MAE 0.31-2.19D) 的高性能.
  • 外部眼部基于照片的模型表现出强的性能 (AUC 0.91-0.99,MAE 0.07-0.18D).

结论:

  • 与眼镜成像集成的深度学习模型显示了折射误差检测的有希望的结果.
  • 基于视网膜和外眼照片的DL模型表现出特别强的性能.
  • 需要进一步评估现实世界的临床实用性和实施策略.