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相关概念视频

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Optical perception, or vision, is an extraordinary sense dependent on converting light signals received via the ocular organs. These organs, known as eyes, are securely positioned within the bony cavities of the skull, called orbits. The orbits serve a dual purpose: a protective shield for the ocular globes and a stable attachment point for the soft ocular tissues. The eye's external protective mechanisms include the eyelids, which are edged with lashes that act as a barrier against foreign...
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Trends in nursing are multifactorial and associated with changes in society, within the nursing profession, and in other professions. Notably, telehealth and remote nursing contribute to successful healthcare delivery for numerous patients and help reduce stress for nurses due to nursing shortages. Nurses can reach patients, monitor their conditions, and interact with them using computers, audio, visual accessories, and telephones—for example, remote patient monitoring systems. Likewise,...
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相关实验视频

Updated: Jun 4, 2025

Techniques for Processing Eyes Implanted With a Retinal Prosthesis for Localized Histopathological Analysis
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人工智能虚拟助理在初级眼科护理实践中

Leandro Stuermer1,2, Sabrina Braga1,2, Raul Martin2,3

  • 1Department of Optometry, University of Contestado, Canoinhas, Brazil.

Ophthalmic & physiological optics : the journal of the British College of Ophthalmic Opticians (Optometrists)
|December 26, 2024
PubMed
概括

一个新的人工智能 (AI) 虚拟助理准确地预测眼睛检查分类,眼睛疾病和双眼视力功能障碍. 这种人工智能工具有助于初级眼科护理和光学教育的决策.

关键词:
人工智能是一种人工智能.临床决策支持 临床决策支持机器学习是机器学习.眼镜测量是指光学测量.虚拟助理是一个虚拟助理.

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科学领域:

  • 眼科诊断 眼科诊断 眼科诊断
  • 医疗保健中的人工智能
  • 临床决策支持系统临床决策支持系统

背景情况:

  • 初级眼睛护理依赖于精确分类折射误差,双眼视力功能障碍和眼部疾病.
  • 光学教育需要有效的工具来培训和发展诊断解释技能.
  • 现有的诊断方法可能耗时,并且可能受益于人工智能驱动的支持.

研究的目的:

  • 开发和评估一种基于人工智能 (AI) 的新型虚拟助理.
  • 为初级眼科医生和光学学生提供决策支持.
  • 在表格式的临床数据上训练人工智能模型,以预测眼科检查结果.

主要方法:

  • 利用来自1125次光学检查 (2250只眼睛) 的匿名临床数据.
  • 训练有素的机器学习算法来分类折射状态,双眼视力和眼部疾病.
  • 采用数据预处理技术,包括一次热编码和SMOTE,火车/测试分成80%/20%.

主要成果:

  • 随机森林算法实现了高性能:>95.2%的眼睛检查分类,>98.1%的眼睛疾病分类,>99.7%的双眼视力功能障碍差异化.
  • 人工智能模型被集成到一个多语言的响应式Web应用程序中,用于直观的临床使用.
  • 性能被严格评估,使用准确度,精度,灵敏度,F1得分和特异性.

结论:

  • 开发了一种有效的AI虚拟助手,用于预测患者分类,眼睛疾病和双眼视力功能障碍.
  • 人工智能助理展示了提高初级眼科护理实践的巨大潜力.
  • 该工具也将成为光学教育课程的宝贵资产.