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光学中的人工智能:当前和未来的前景.

Anantha Krishnan1, Ananya Dutta2, Alok Srivastava3,4

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人工智能 (AI) 为提高眼科护理效率和解决全球眼科专业人员短缺问题提供了巨大的潜力. 然而, optometry 中的许多 AI 模型在小数据集和过度配合方面遇到了困难,这限制了它们的临床应用.

关键词:
人工智能应用程序AI应用程序人工智能的人工智能是人工智能.眼镜测量是指光学测量.

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科学领域:

  • 眼科和眼镜检查的眼科和眼镜检查
  • 医疗保健中的人工智能
  • 医疗信息学 医疗信息学

背景情况:

  • 全球眼睛护理面临着由于专业人员短缺和视力障碍日益增加的挑战.
  • 人工智能 (AI) 有潜力提高光度工作流程的效率和决策.
  • 需要创新的解决方案来弥合眼科护理服务的差距,特别是在服务不足的地区.

研究的目的:

  • 审查AI在光学中的应用,重点关注其优化患者护理的潜力.
  • 分析光学中使用的AI模型,考虑模型选择,数据大小,评估指标和可解释性.
  • 在光学设置中识别当前人工智能模型的优缺点.

主要方法:

  • 在关键光学领域应用AI模型的系统审查.
  • 对模型选择 (CNN,转移学习,RF,SVM,XGBoost) 和数据集参数的分析.
  • 使用准确度指标对模型性能进行评估,并评估过拟合和可解释性.

主要成果:

  • 基于图像的AI研究主要使用CNN或转移学习;临床数据研究使用RF,SVM和XGBoost.
  • 较大的数据集通常导致更高的准确性,但许多光学人工智能模型遭受了不足的培训和验证样本大小.
  • 在一些研究中观察到过度装配,20%的人报告精度低于80%,影响了临床效用.

结论:

  • 人工智能有望改善光学,但目前的模型需要更大的数据集和强大的验证来提高准确性和可靠性.
  • 解决诸如小样本大小和过度配合等局限性对于人工智能在眼科护理中的成功临床实施至关重要.
  • 本综述为眼科医生提供了关于人工智能的能力和局限性的见解,指导实践中的知情采用.