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Inducement and Evaluation of a Murine Model of Experimental Myopia

Inducement and Evaluation of a Murine Model of Experimental Myopia

Published on: January 22, 2019

基于血液参数的可解释机器学习模型用于查高近视.

Zhengwei Yang^1,2, Manqiao Wang¹, Xinyuan Huang¹

¹Tianjin Key Laboratory of Retinal Functions and Diseases, Tianjin Branch of National Clinical Research Center for Ocular Disease, Eye Institute and School of Optometry, Tianjin Medical University Eye Hospital, Tianjin, China.

Eye (London, England)

|November 25, 2025

概括

此摘要是机器生成的。

使用常规血液检查的新机器学习 (ML) 模型可以选高近视 (HM). 这种可访问和可解释的工具为早期HM检测提供了成本效益高的替代方案,特别是在资源有限的地区.

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科学领域:

眼科医生眼科眼科
生物医学信息学生物医学信息学
临床诊断临床诊断临床诊断

背景情况:

高近视 (HM) 是一个重大的公共卫生挑战.
目前对HM的查方法可能不方便且昂贵.
需要易于使用和具有成本效益的HM选工具.

研究的目的:

开发一种可解释的机器学习 (ML) 模型,用于高近视 (HM) 查.
用例行血液参数作为ML模型的基础.
建立一个方便且具有成本效益的替代传统的HM选方法.

主要方法:

一项横截面研究包括313名参与者 (158名HM,155名非HM).
使用单变量分析和拉索回归来进行特征选择.
训练和验证了8个ML算法,其中极端梯度提升 (XGBoost) 被确定为最佳. 使用AUC,精度,灵敏度,特异性和DCA.来评估性能. 使用SHAP分析来确定特征的重要性.

主要成果:

确定了八个关键的血液参数和人口因素:直接胆红素 (DBIL),总胆红素 (TBIL),白蛋白 (ALB),性酸酶 (ALP),年龄,葡萄糖 (GLU),肌素 (CREA) 和尿酸 (UA).
在五倍交叉验证中,最佳的XGBoost模型实现了高性能,AUC值为0.954 (训练),0.822 (验证) 和0.898 (测试).
SHAP分析证实了模型的可解释性和临床实用性,通过DCA证明了良好的校准和决策实用性.

结论:

一个基于血液的ML模型是可行的查高近视 (HM).
这种可解释的模型为早期HM检测提供了一种可访问和具有成本效益的工具.
该模型显示出在资源有限的环境中实现的前景.