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

相关实验视频

Updated: Jul 12, 2025

Binocular Dynamic Visual Acuity in Eyeglass-Corrected Myopic Patients
07:06

Binocular Dynamic Visual Acuity in Eyeglass-Corrected Myopic Patients

Published on: March 29, 2022

2.6K

机器学习模型用于预测高近视眼中的长期视觉敏度.

Yining Wang1, Ran Du1,2, Shiqi Xie1

  • 1Department of Ophthalmology and Visual Science, Tokyo Medical and Dental University, Tokyo, Japan.

JAMA ophthalmology
|October 26, 2023
PubMed
概括

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

Stage-specific disruption of erythropoiesis leads to anemia in newly diagnosed multiple myeloma patients.

Frontiers in cell and developmental biology·2026
Same author

A S.C.O.R.E. framework for evaluating open-ended responses from large language models in healthcare.

Cell reports. Medicine·2026
Same author

The discriminating ability of multiple body composition measures in assessing cardiorespiratory fitness in Chinese adolescents aged 12-18.

Frontiers in nutrition·2026
Same author

Corrigendum to "Oculomics and AI: The eye as a biomarker for health span" [Asia-Pac J Ophthalmol 15 (1) (2026) 100282].

Asia-Pacific journal of ophthalmology (Philadelphia, Pa.)·2026
Same author

How to meaningfully evaluate AI in clinical medicine.

Nature medicine·2026
Same author

Correction to: CD44‑targeted therapy using mP6/Rg3 micelles inhibits oral cancer stem cell proliferation and migration.

Cell biology and toxicology·2026

人工智能模型可以预测高近视患者未来的视力敏度. 这些工具有助于识别面临视力丧失风险的个体,从而实现积极的临床管理.

科学领域:

  • 眼科医生 眼科 眼科
  • 人工智能的人工智能
  • 机器学习 机器学习

背景情况:

  • 高近视是一种日益增长的全球健康挑战,增加了病态近视导致严重视力损失的风险.
  • 早期识别面临视力减弱风险的患者对于有效管理至关重要.

研究的目的:

  • 开发和评估机器学习模型,用于预测高近视症患者在3岁和5岁时的视敏度 (VA).
  • 评估模型在5年内预测视力障碍风险的能力.

主要方法:

  • 一项回顾性队列研究分析了967名患者 (1,616只眼睛) 的数据,这些患者在3岁和5岁时已知最佳纠正VA (BCVA).
  • 用34个临床和成像变量来训练回归和分类模型.
  • 使用歧视指标,校准和决策曲线分析评估模型性能;使用可解释AI确定变量重要性.

主要成果:

  • 支持矢量机和随机森林模型在3年和5年后对BCVA显示出强大的预测性能.
  • 一种逻辑回归模型有效地预测了5年后视力损伤的风险 (AUC = 0.870).
  • 视力障碍的关键预测因素包括基线BCVA,先前的近视性黄斑新血管化,年龄和近视性黄斑病变的严重程度.

结论:

更多相关视频

Inducement and Evaluation of a Murine Model of Experimental Myopia
07:20

Inducement and Evaluation of a Murine Model of Experimental Myopia

Published on: January 22, 2019

9.9K
In vivo Structural Assessments of Ocular Disease in Rodent Models using Optical Coherence Tomography
07:44

In vivo Structural Assessments of Ocular Disease in Rodent Models using Optical Coherence Tomography

Published on: July 24, 2020

3.0K

相关实验视频

Last Updated: Jul 12, 2025

Binocular Dynamic Visual Acuity in Eyeglass-Corrected Myopic Patients
07:06

Binocular Dynamic Visual Acuity in Eyeglass-Corrected Myopic Patients

Published on: March 29, 2022

2.6K
Inducement and Evaluation of a Murine Model of Experimental Myopia
07:20

Inducement and Evaluation of a Murine Model of Experimental Myopia

Published on: January 22, 2019

9.9K
In vivo Structural Assessments of Ocular Disease in Rodent Models using Optical Coherence Tomography
07:44

In vivo Structural Assessments of Ocular Disease in Rodent Models using Optical Coherence Tomography

Published on: July 24, 2020

3.0K
  • 使用临床和成像数据,开发人工智能驱动的模型来预测高近视的长期视力敏度是可行的.
  • 这些预测模型为高近视患者的临床评估和主动管理提供了宝贵的工具.