Jove
Visualize
联系我们
JoVE
x logofacebook logolinkedin logoyoutube logo
关于 JoVE
概览领导团队博客JoVE 帮助中心
作者
出版流程编辑委员会范围与政策同行评审常见问题投稿
图书馆员
用户评价订阅访问资源图书馆顾问委员会常见问题
研究
JoVE JournalMethods CollectionsJoVE Encyclopedia of Experiments存档
教育
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab Manual教师资源中心教师网站
使用条款与条件
隐私政策
政策

相关概念视频

Depth Perception and Spatial Vision01:15

Depth Perception and Spatial Vision

Depth perception is the ability to perceive objects three-dimensionally. It relies on two types of cues: binocular and monocular. Binocular cues depend on the combination of images from both eyes and how the eyes work together. Since the eyes are in slightly different positions, each eye captures a slightly different image. This disparity between images, known as binocular disparity, helps the brain interpret depth. When the brain compares these images, it determines the distance to an object.

您也可能阅读

相关文章

通过共同作者、期刊和引用图与本文相关的文章。

排序
Same author

Differential Impact of Intraocular Pressure Lowering on Glaucoma Progression in African and European Descent Individuals.

American journal of ophthalmology·2026
Same author

Image-Quality-Aware Multimodal Artificial Intelligence for Automated Structured OCT Report Generation in Glaucoma Evaluation.

Ophthalmology science·2026
Same author

Intraocular Pressure Measurement Variability in the Ocular Hypertension Treatment Study.

Ophthalmology. Glaucoma·2026
Same author

Toward Long-Term Visual Field Appearance Forecasting Using Artificial Intelligence for Ophthalmic Education and Diagnosis.

Ophthalmology science·2026
Same author

Association of Deep Optic Nerve Head Structural Remodeling with Choroidal Microvasculature Dropout in Glaucoma with and without Myopia.

American journal of ophthalmology·2026
Same author

Glaucomatous Remodeling of the Lamina Cribrosa: Association With Visual Field Progression.

Investigative ophthalmology & visual science·2026

相关实验视频

Updated: Jun 17, 2026

Morphometric Analyses of Retinal Sections
14:33

Morphometric Analyses of Retinal Sections

Published on: February 19, 2012

10.2K

深度学习方法预测纵向视网膜神经纤维层厚度变化

Jalil Jalili1, Evan Walker1, Christopher Bowd1

  • 1Hamilton Glaucoma Center and Division of Ophthalmology Informatics and Data Science, Shiley Eye Institute, Viterbi Family Department of Ophthalmology, University of California, San Diego, CA 92037, USA.

Bioengineering (Basel, Switzerland)
|February 26, 2025
PubMed
概括

深度学习模型准确地预测视网膜神经纤维层 (RNFL) 厚度变化. 这些模型,特别是1D卷积神经网络 (CNN),有助于早期的青光眼诊断和监测疾病进展.

关键词:
在RNFL厚度预测预测.深度学习是一种深度学习.玻璃眼 glaucoma 玻璃眼 玻璃眼 玻璃眼 玻璃眼纵向的OCT是一种OCT一维卷积神经网络的一个维度.光学连贯性断层扫描技术

更多相关视频

Using Retinal Imaging to Study Dementia
09:17

Using Retinal Imaging to Study Dementia

Published on: November 6, 2017

21.4K
Author Spotlight: Unraveling the Pathogenesis of Age-Related Macular Degeneration and Discovering Potential Therapies
06:16

Author Spotlight: Unraveling the Pathogenesis of Age-Related Macular Degeneration and Discovering Potential Therapies

Published on: July 28, 2023

2.4K

相关实验视频

Last Updated: Jun 17, 2026

Morphometric Analyses of Retinal Sections
14:33

Morphometric Analyses of Retinal Sections

Published on: February 19, 2012

10.2K
Using Retinal Imaging to Study Dementia
09:17

Using Retinal Imaging to Study Dementia

Published on: November 6, 2017

21.4K
Author Spotlight: Unraveling the Pathogenesis of Age-Related Macular Degeneration and Discovering Potential Therapies
06:16

Author Spotlight: Unraveling the Pathogenesis of Age-Related Macular Degeneration and Discovering Potential Therapies

Published on: July 28, 2023

2.4K

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:

  • 青光眼的诊断和监测依赖于检测结构变化,例如视网膜神经纤维层 (RNFL) 变薄.
  • 纵向数据分析对于了解疾病进展模式至关重要.

研究的目的:

  • 开发和评估深度学习 (DL) 模型,用于预测青光眼患者的RNFL厚度变化.
  • 为了比较DL模型的性能与RNFL厚度预测的传统回归方法.

主要方法:

  • 利用了两项研究 (DIGS和ADAGES) 中251名青光瘤患者的纵向光学连贯性断层扫描 (OCT) 数据.
  • 训练并评估了四种模型:线性回归 (LR),支向量回归 (SVR),梯度增强回归 (GBR) 和定制的1D卷积神经网络 (CNN).
  • 采用患者级数据分割,以进行可靠的评估,并使用平均绝对误差 (MAE) 和R平方 (R2) 评估预测准确度.

主要成果:

  • 梯度增强回归 (GBR) 模型在预测点向RNFL厚度变化方面表现强 (MAE = 5.2μm,R2 = 0.91).
  • 定制的1D CNN在预测平均全球和部门RNFL厚度变化方面取得了卓越的结果 (MAE从2.0-4.2μm,R2从0.94-0.98).
  • 定制的DL模型在不同的人口统计和疾病严重程度方面表现一致.

结论:

  • 深度学习模型,特别是定制的1D CNN,为准确和高分辨率的RNFL厚度变化预测提供了一个有希望的方法.
  • 这些模型有可能作为临床决策支持工具,用于更早地诊断青光眼并改善疾病管理.
  • 在DL模型中整合纵向OCT成像提高了它们在随时间跟踪绿眼病进展方面的可靠性.