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

Glaucoma: Overview01:25

Glaucoma: Overview

532
Glaucoma is an eye condition characterized by increased intraocular pressure that damages the retina and optic nerve, leading to irreversible blindness if left untreated. The human eye has various components, including the cornea, iris, pupil, lens, and optic nerve. Aqueous humor is secreted by the epithelium of the ciliary body in the posterior chamber and flows through the trabecular meshwork and canal of Schlemm, maintaining normal intraocular pressure. The trabecular meshwork and the canal...
532

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

Genetic markers of stomatal cluster development in Begoniaceae revealed through trait analysis assisted by interactive deep-learning.

Plant physiology·2026
Same author

Perimetric Outcomes of Melbourne Rapid Field Perimetry in Patients With Glaucoma: A Systematic Review and Meta-Analysis.

Journal of glaucoma·2026
Same author

Firecracker injury induced acute secondary angle closure.

Eye (London, England)·2026
Same author

Authors' Response to Comment on: "Travel and financial burdens of cataract surgical care in South India: Comparison of postoperative follow-up at local vision centers versus an urban eye hospital".

Indian journal of ophthalmology·2026
Same author

Efficacy and Safety of DOACs in Patients with Atrial Fibrillation and History of Falls or Risk of Falls: The Liverpool AF-Falls Project. A Systematic Review and Bayesian Network Meta-analysis.

Drugs & aging·2026
Same author

Family Screening in Glaucoma: A Scoping Review.

Ophthalmology. Glaucoma·2026

相关实验视频

Updated: Jun 18, 2025

Assessing Early Stage Open-Angle Glaucoma in Patients by Isolated-Check Visual Evoked Potential
07:11

Assessing Early Stage Open-Angle Glaucoma in Patients by Isolated-Check Visual Evoked Potential

Published on: May 25, 2020

6.3K

EffUnet-SpaGen:一种高效和空间生成的方法来检测青光眼.

Venkatesh Krishna Adithya1, Bryan M Williams2, Silvester Czanner3

  • 1Department of Glaucoma, Aravind Eye Care System, Thavalakuppam, Pondicherry 605007, India.

Journal of imaging
|July 31, 2024
PubMed
概括

一个新的青光眼检测算法,EffUnet-SpaGen,使用高效的细分和空间几何建模来实现高精度,减少计算需求. 这种更苗条的模型可以快速对新数据进行重新校准,从而提高临床采用率.

关键词:
这是分类分类的分类.诊断 诊断 诊断 诊断 诊断 诊断生成型模型的生成型模型.玻璃眼 glaucoma 玻璃眼 玻璃眼 玻璃眼 玻璃眼机器学习是机器学习.

更多相关视频

Full-Circle Cauterization of Limbal Vascular Plexus for Surgically Induced Glaucoma in Rodents
10:10

Full-Circle Cauterization of Limbal Vascular Plexus for Surgically Induced Glaucoma in Rodents

Published on: February 15, 2022

1.4K
Topographical Estimation of Visual Population Receptive Fields by fMRI
06:02

Topographical Estimation of Visual Population Receptive Fields by fMRI

Published on: February 3, 2015

9.2K

相关实验视频

Last Updated: Jun 18, 2025

Assessing Early Stage Open-Angle Glaucoma in Patients by Isolated-Check Visual Evoked Potential
07:11

Assessing Early Stage Open-Angle Glaucoma in Patients by Isolated-Check Visual Evoked Potential

Published on: May 25, 2020

6.3K
Full-Circle Cauterization of Limbal Vascular Plexus for Surgically Induced Glaucoma in Rodents
10:10

Full-Circle Cauterization of Limbal Vascular Plexus for Surgically Induced Glaucoma in Rodents

Published on: February 15, 2022

1.4K
Topographical Estimation of Visual Population Receptive Fields by fMRI
06:02

Topographical Estimation of Visual Population Receptive Fields by fMRI

Published on: February 3, 2015

9.2K

科学领域:

  • 医疗成像医学成像
  • 医疗保健中的人工智能
  • 眼科医生 眼科 眼科

背景情况:

  • 自动疾病检测算法越来越多地专注于开发"更苗条"的模型.
  • 这些模型旨在减少对广泛训练数据集的需求,并加快对新数据的重新校准,同时保持高精度.
  • 开发更苗条的模型是医学成像中的重要研究趋势.

研究的目的:

  • 开发一种新,高效,准确的双相自动化玻璃眼瘤检测算法.
  • 识别和利用 fundus 图像数据中的几何冗余,以改善青光眼的诊断.
  • 创建一个计算效率高,易于适应新数据集的模型.

主要方法:

  • 开发了一种新的杯子和磁盘细分算法"EffUnet",采用高效的卷积块.
  • "EffUnet"与扩展空间生成方法"SpaGen"的整合,用于几何建模和分类.
  • 通过仅对EffUnet层进行重新校准来演示快速模型训练.

主要成果:

  • 该EffUnet算法在分割光盘和杯边界方面取得了很高的准确性.
  • 结合的"EffUnet-SpaGen"算法超越了最先进的玻璃眼瘤检测方法,获得了0.997 (ORIGA) 和0.969 (DRISHTI) 的AUROC分数.
  • 该算法通过可视化变形的光圈区域来提供可解释性,这对于临床实施至关重要.

结论:

  • "EffUnet-SpaGen"算法显著降低了在青光眼检测中的计算负担.
  • 与现有方法相比,该模型显示出更高的准确性和效率.
  • 可解释性功能增强了自动化玻璃眼瘤检测系统的临床采用和实施的潜力.