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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.
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Angle-closure glaucoma, or closed-angle glaucoma, is an eye condition where the iris bulges out and blocks the iridocorneal angle, resulting in a buildup of aqueous humor and increased intraocular pressure. Immediate medical attention is necessary due to the sudden onset of symptoms. The treatment for angle-closure glaucoma includes short-term and long-term approaches. Short-term treatment involves using eye drops like pilocarpine to lower intraocular pressure by increasing aqueous humor...
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机器学习的进步,用于诊断角.

Zahra J Muhsin1, Rami Qahwaji2, Ibrahim Ghafir1

  • 1Faculty of Engineering and Digital Technologies, University of Bradford, Bradford, BD7 1DP, UK.

International ophthalmology
|March 30, 2025
PubMed
概括

机器学习 (ML) 对诊断角 (KC) 是有前途的,但研究和临床应用之间存在差距. 克服标准化和数据访问方面的挑战是将ML整合到眼科护理中的关键.

关键词:
角膜成像方式的模式.检测 检测 检测 检测 检测早期诊断 早期诊断 早期诊断眼科医生 眼科 眼科严重程度的分阶段化

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科学领域:

  • 眼科医生 眼科 眼科
  • 医疗信息学 医疗信息学
  • 人工智能的人工智能

背景情况:

  • 角 (KC) 诊断依赖于各种临床和成像数据.
  • 机器学习 (ML) 提供了改善KC检测和分期的潜力.
  • 学术ML研究与其在眼科中的临床应用之间仍然存在差距.

研究的目的:

  • 在过去的十年中,审查机器学习 (ML) 在角 (KC) 诊断中的应用.
  • 识别KC的ML的进步,共同的方法和挑战.
  • 突出ML研究和临床实施之间的差距.

主要方法:

  • 对KC诊断中的ML数字图书馆的系统文献搜索.
  • 包括/排除标准适用于62个已识别的物品.
  • 分析的重点是ML算法,成像模式,数据集和KC条件.

主要成果:

  • 监督分类器主导KC诊断 (97%),随机森林最常见.
  • Pentacam是领先的成像模式 (56%);大多数研究使用本地数值数据 (91%).
  • 研究主要比较正常眼睛与角眼睛,仅限于对KC严重程度阶段的关注 (20%).

结论:

  • 缺乏关于早期KC检测标准和严重程度分阶段的共识,阻碍了进展.
  • 有限的多学科协作和对公共数据集的限制性访问是关键障碍.
  • 需要进一步的研究和路线图模型来将ML纳入KC诊断中的临床整合.