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

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

Deep Learning to Predict Geographic Atrophy Area and Growth Rate from Multimodal Imaging.

Ophthalmology. Retina·2022
Same author

Spectrally resolved autofluorescence imaging in posterior uveitis.

Scientific reports·2022
Same author

[Erratum to: First clinical results with the PAUL® Glaucoma Implant at the University Eye Hospital Bonn].

Die Ophthalmologie·2022
Same author

A CONSENSUS ON RISK MITIGATION FOR BROLUCIZUMAB IN NEOVASCULAR AGE-RELATED MACULAR DEGENERATION: Patient Selection, Evaluation, and Treatment.

Retina (Philadelphia, Pa.)·2022
Same author

Histologic Cell Shape Descriptors for the Retinal Pigment Epithelium in Age-Related Macular Degeneration: A Comparison to Unaffected Eyes.

Translational vision science & technology·2022
Same author

The Feasibility of Using Ultra-Widefield Retinal Imaging to Identify Ocular Pathologies amongst Those with Systemic Medical Disease Attending a Tertiary Healthcare Facility at a University Hospital.

Ophthalmologica. Journal international d'ophtalmologie. International journal of ophthalmology. Zeitschrift fur Augenheilkunde·2022

相关实验视频

Updated: Jul 1, 2025

Quantitative Fundus Autofluorescence for the Evaluation of Retinal Diseases
07:22

Quantitative Fundus Autofluorescence for the Evaluation of Retinal Diseases

Published on: March 11, 2016

11.5K

个性化的镜头校正改善了定量底部自光分析的分析.

Leon von der Emde1, Geena C Rennen1, Marc Vaisband2,3

  • 1Department of Ophthalmology, University Hospital Bonn, Bonn, Germany.

Investigative ophthalmology & visual science
|March 11, 2024
PubMed
概括

使用透镜自光 (LQAF) 的个性化公式与基于年龄的校正相比,显著提高了定量底部自光 (QAF) 的准确性. 这种方法增强了QAF成像解释,特别是在老年人中.

更多相关视频

Author Spotlight: Understanding Age-Related Macular Degeneration Pathophysiology with QAF Workflow
08:54

Author Spotlight: Understanding Age-Related Macular Degeneration Pathophysiology with QAF Workflow

Published on: May 26, 2023

1.4K
Author Spotlight: Advancements in Refractive Surgical Correction for Presbyopia and Exploring Postoperative Visual Acuity
05:46

Author Spotlight: Advancements in Refractive Surgical Correction for Presbyopia and Exploring Postoperative Visual Acuity

Published on: September 20, 2024

428

相关实验视频

Last Updated: Jul 1, 2025

Quantitative Fundus Autofluorescence for the Evaluation of Retinal Diseases
07:22

Quantitative Fundus Autofluorescence for the Evaluation of Retinal Diseases

Published on: March 11, 2016

11.5K
Author Spotlight: Understanding Age-Related Macular Degeneration Pathophysiology with QAF Workflow
08:54

Author Spotlight: Understanding Age-Related Macular Degeneration Pathophysiology with QAF Workflow

Published on: May 26, 2023

1.4K
Author Spotlight: Advancements in Refractive Surgical Correction for Presbyopia and Exploring Postoperative Visual Acuity
05:46

Author Spotlight: Advancements in Refractive Surgical Correction for Presbyopia and Exploring Postoperative Visual Acuity

Published on: September 20, 2024

428

科学领域:

  • 眼科医生 眼科 眼科
  • 医疗成像医学成像
  • 生物医学光学 生物医学光学

背景情况:

  • 目前的定量基底自光 (QAF) 校正依赖于年龄,由于镜头变暗的个体变化,这不足.
  • 天生的透镜自光不被考虑在现有的QAF校正方法中,限制了老年人群的准确性.

研究的目的:

  • 开发和比较QAF校正的个性化公式,以考虑镜头变暗.
  • 评估镜头定量自光 (LQAF) 和Scheimpflug成像对QAF值的预测值.

主要方法:

  • 130名参与者的横截面研究,其中一小组接受多式成像 (Scheimpflug,AC-OCT,LQAF,QAF) 的白内障前后手术.
  • 使用LASSO回归和逆向选择进行统计分析,以确定可预测的透镜参数,然后使用线条混合模型来量化对QAF的影响.

主要成果:

  • 镜头定量自光 (LQAF) 和Scheimpflug测量是QAF最相关的预测指标,增加的值与QAF下降相关.
  • 与当前基于年龄的公式 (MAE 96.1 ± 93.5) 相比,个性化支线模型实现了明显较低的预测误差 (MAE 32.2 ± 23.4).
  • 无论是LQAF (P < 0.01) 还是Scheimpflug (P < 0.001) 都是线混合模型中的重要因素.

结论:

  • 镜头定量自光 (LQAF) 成像对自然镜头对QAF成像的影响具有很高的预测性.
  • 在临床实践中实施个性化的透镜分数可以提高QAF解释准确度.
  • 这种方法可能使老年患者在未来的QAF研究中被纳入.