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在红光疗法中作为近视控制的关键预测因子的轴向和胆道变化:来自机器学习模型的证据

Xiaoxue Hu¹, Zixun Wang², Rui Li³

¹Wuhan Children's Hospital (Wuhan Maternal and Child Healthcare Hospital), Tongji Medical College, Huazhong University of Science & Technology, Wuhan, Hubei, China.

Photodiagnosis and photodynamic therapy

|January 9, 2026

概括

重复的低水平红光疗法对儿童近视控制有希望. 机器学习模型准确地预测治疗结果,使用诸如轴长和胆道厚度变化等因素.

科学领域:

眼科医生眼科眼科
生物医学工程生物医学工程
数据科学数据科学数据科学

背景情况:

重复的低水平红光疗法 (RLRL) 是一种新的,非侵入性的近视控制方法.
在儿科近视症中RLRL疗效的预测因素需要进一步研究.

研究的目的:

开发和验证可解释的机器学习模型,用于预测儿童RLRL治疗的短期和长期结果.
为了确定RLRL治疗疗效的关键预测因素.

主要方法:

一项多中心队列研究,涉及538名儿科患者,随访时间至少为一年.
应用多种特征选择方法 (LASSO,Boruta等) 和七个机器学习算法.
使用SHAP和LIME进行模型解释性和性能评估 (AUC,精度,F1分数).

主要成果:

XGBoost显示出最佳的预测性能 (AUC: 0.90-0.92).
确定了关键预测因素:年龄,基线轴长 (AL),前腔深 (ACD),RNFL厚度,AL变化 (ChangeAL) 和胸腔厚度变化 (ChangeChT).
ChangeAL是主要的短期预测因素;ChangeChT对长期结果的影响最大. 年龄的增长和胆道结厚的增加显示出保护作用.

结论:

一个可解释的机器学习模型准确地预测了儿科近视的RLRL治疗结果.
ChangeAL和ChangeChT是监测治疗反应的关键动态生物标志物.
研究结果支持针对近视管理的个性化临床决策.

关键词:

近视近视近视近视近视近视近视近视近视近视近视近视近视近视近视近视近视近视近视近视近视近视近视近视近视近视近视近视近视近视近视近视近视近视近视近视近视近视近视近视近视近轴心长度轴心长度轴心长度轴心长度冠状腺厚度冠状腺厚度机器学习是机器学习.预测模型预测模型重复的低水平红光 (RLRL)

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