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Inducement and Evaluation of a Murine Model of Experimental Myopia
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使用机器学习预测近视:外部验证研究

Rajat S Chandra1, Bole Ying2, Jianyong Wang3

  • 1Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA.

Vision (Basel, Switzerland)
|October 24, 2025
PubMed
概括
此摘要是机器生成的。

机器学习模型可以准确地预测使用非cycloplegic数据的学生的循环折射误差和近视. 这种外部验证证实了ML的ML.

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循环折的折射方式机器学习是机器学习.短视近视近视近视近视近视近视近视近预测 预测 预测 预测验证验证的时间

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科学领域:

  • 眼科医生 眼科 眼科
  • 人工智能的人工智能
  • 生物医学工程 生物医学工程

背景情况:

  • 机器学习 (ML) 模型以前被开发用于在标准化条件下使用非cycloplegic数据来预测循环球等效折射 (SER) 和近视.
  • 这些ML模型在不同的临床环境中与不同的循环病剂和生物识别设备的通用性是不确定的.

研究的目的:

  • 评估ML模型在预测中国学生独立队列中循环 SER和近视状态方面的表现.
  • 评估模型的稳定性,尽管周期性药物 (热皮卡米德,环酸盐) 和生物识别设备 (IOLMaster 700,SW-9000) 的变化.

主要方法:

  • 研究了614名8至13岁的中国学生的独立队列.
  • 自律折射在循环之前和之后使用0.5%的热胺或1%的循环酸盐进行.
  • 使用IOLMaster 700或光学生物计SW-9000进行了生物识别测量.
  • 使用R2,平均绝对误差 (MAE),灵敏度,特异性和ROC曲线下的面积 (AUC) 评估了ML模型 (XGBoost,随机森林).

主要成果:

  • 该XGBoost模型显示了循环性SER的优异预测 (R2 = 0.95,MAE = 0.32 D).
  • 两种ML模型都准确地预测了近视状态 (随机森林:AUC 0.99;XGBoost:AUC未指定) 和近视患病率 (观察62.9%;预测58.8%-60.6%).
  • 通过这两种模型实现了近视预测的高灵敏度和特异性,即使使用异质循环和生物识别数据.

结论:

  • XGBoost和随机森林ML模型在独立队列中使用非cycloplegic数据有效预测周期性SER和近视状态.
  • 外部验证证实,ML模型可以可靠地估计周期性SER和近视患病率,尽管临床参数异质.
  • 建议对不同人群进行进一步的研究,以巩固ML工具在眼科评估中的实用性.