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

Open Angle Glaucoma: Treatment01:27

Open Angle Glaucoma: Treatment

391
In open-angle glaucoma, the iridocorneal angle remains open, but the trabecular meshwork becomes stiff, slowing down the outflow of aqueous humor. This causes a buildup of aqueous humor in the anterior chamber, leading to a sudden increase in intraocular pressure. The treatment for open-angle glaucoma focuses on reducing the elevated intraocular pressure by either decreasing the secretion of aqueous humor or increasing its outflow.
Drugs such as carbonic anhydrase inhibitors, α2- and...
391

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

A qualitative study on the influencing factors of the quality of life of children aged 7-14 with allergic diseases from the perspective of caregivers.

BMC nursing·2026
Same author

Niraparib treatment patterns and outcomes in first-line maintenance therapy for ovarian cancer: The RENI-1 study.

Chinese medical journal·2026
Same author

Effect of a Quality Management System on Preventing Peripherally Inserted Central Catheter-Related Complications Among Pediatric Patients With Hematology Disorders.

Cancer nursing·2026
Same author

Comparative effectiveness of inspiratory muscle training approaches within intensive care unit-acquired weakness prevention and rehabilitation strategies: a systematic review and component network meta-analysis.

BMC pulmonary medicine·2026
Same author

From Pathway Tracing to Actionable Targets: Integrative Mendelian Randomization and Experimental Triangulation Map Metabolic Pathways Across Ovarian Cancer Histotypes.

International journal of molecular sciences·2026
Same author

Arginine restriction exploits DNMT3B-ASS1-driven arginine auxotrophy and mTORC1-suppressed autophagy to overcome niraparib resistance in ovarian cancer.

Cancer letters·2026

相关实验视频

Updated: Jun 6, 2025

Translaminar Autonomous System Model for the Modulation of Intraocular and Intracranial Pressure in Human Donor Posterior Segments
08:55

Translaminar Autonomous System Model for the Modulation of Intraocular and Intracranial Pressure in Human Donor Posterior Segments

Published on: April 24, 2020

3.0K

预测24小时眼内压力变化的机器学习模型:一项比较研究

Chen Ranran1,2, Lei Jinming3, Liao Yujie1,2

  • 1Department of Ophthalmology, Shanghai Fifth People's Hospital, Fudan University, Shanghai, China.

Medical science monitor : international medical journal of experimental and clinical research
|December 3, 2024
PubMed
概括
此摘要是机器生成的。

机器学习使用白天测量准确预测24小时眼内压力 (IOP) 波动. XGBoost模型显示了改善青光眼治疗和临床应用的巨大潜力.

更多相关视频

A Model of Glaucoma Induced by Circumlimbal Suture in Rats and Mice
07:00

A Model of Glaucoma Induced by Circumlimbal Suture in Rats and Mice

Published on: October 5, 2018

10.4K
A Laser-induced Mouse Model of Chronic Ocular Hypertension to Characterize Visual Defects
07:00

A Laser-induced Mouse Model of Chronic Ocular Hypertension to Characterize Visual Defects

Published on: August 14, 2013

13.0K

相关实验视频

Last Updated: Jun 6, 2025

Translaminar Autonomous System Model for the Modulation of Intraocular and Intracranial Pressure in Human Donor Posterior Segments
08:55

Translaminar Autonomous System Model for the Modulation of Intraocular and Intracranial Pressure in Human Donor Posterior Segments

Published on: April 24, 2020

3.0K
A Model of Glaucoma Induced by Circumlimbal Suture in Rats and Mice
07:00

A Model of Glaucoma Induced by Circumlimbal Suture in Rats and Mice

Published on: October 5, 2018

10.4K
A Laser-induced Mouse Model of Chronic Ocular Hypertension to Characterize Visual Defects
07:00

A Laser-induced Mouse Model of Chronic Ocular Hypertension to Characterize Visual Defects

Published on: August 14, 2013

13.0K

科学领域:

  • 眼科医生 眼科 眼科
  • 医疗信息学 医疗信息学
  • 机器学习 机器学习

背景情况:

  • 预测24小时内眼压 (IOP) 波动对于有效的青光眼治疗至关重要.
  • 传统的24小时IOP监测方法复杂且有局限性.
  • 机器学习为预测IOP波动提供了一种新的方法.

研究的目的:

  • 开发和评估用于预测24小时IOP波动的机器学习模型.
  • 为了确定准确的IOP波动预测的关键特征.
  • 在这个任务中评估不同机器学习算法的性能.

主要方法:

  • 使用二进制分类方法来分类IOP波动 (>8 mmHg或≤8 mmHg).
  • 分析了24小时IOP监测数据,包括22个特征.
  • 特征选择采用了千平方测试和点-双序列相关性,显著水平为P<1,P<0.1,P<0.05,P<0.025.
  • 采用了五种二进制分类算法,使用准确度,特异性,精度,灵敏度,F1评分,AUC和AUCPR来评估性能.
  • 沙普利添加物解释 (SHAP) 用于特征重要性评估.

主要成果:

  • 使用P<0.05特征的模型与其他子集相比,表现优越.
  • XGBoost算法实现了最高的性能指标.
  • XGBoost的准确度为0.886,特异性为0.972,精度为0.857,灵敏度为0.585,F1得分为0.696,AUC为0.890,AUCCPR为0.794. XGBoost的准确度为0.886,特异性为0.972,精度为0.857,灵敏度为0.585,F1得分为0.696,AUC为0.890,AUC为0.794.
  • 使用SHAP值进行了特征重要性分析.

结论:

  • 机器学习算法可以有效地预测24小时的IOP波动.
  • 该XGBoost算法显示显著的希望,用于临床应用在绿眼管理.
  • 这种预测能力可以提高患者护理和治疗眼的治疗策略.