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定量色底摄影参数作为轴长度进展的潜在生物标志物:来自机器学习队列研究的证据
Zixun Wang1, Feifei Han1, Xiaoling Zhang2,1
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.
Frontiers in cell and developmental biology
|February 11, 2026
概括
一个新的机器学习模型准确地预测使用视网膜成像的儿童近视的进展. 这种方法可以早期识别有风险的儿童,以便及时进行近视控制干预.
科学领域:
- 眼科医生 眼科 眼科
- 生物医学工程 生物医学工程
- 人工智能的人工智能
背景情况:
- 早期识别患有加速轴延长风险的儿童对于有效的近视控制至关重要.
- 彩色底部摄影 (CFP) 的定量参数可以提供关于轴长度 (AL) 进展的见解.
- 这些CFP衍生生物标志物的预测价值需要进一步描述.
研究的目的:
- 开发和验证一种机器学习模型,用于预测学龄儿童1年的AL进展.
- 将CFP定量生物标志物与临床数据相结合,以提高预测能力.
- 通过视网膜成像和临床因素确定AL进展的关键预测因素.
主要方法:
- 一项对693名6至10岁儿童在中国天津的队列研究.
- 利用了144个定量CFP指标和基线临床变量.
- 采用LASSO回归,物流回归和专家评估来选择特征,随机森林 (RF) 作为所选择的算法,通过交叉验证和网格搜索进行优化.
主要成果:
- 457名儿童 (65.9%) 显示显著的AL进展 (>0.2毫米).
- 射频模型实现了高预测性能,AUC为0.961.1.
- 关键预测因素包括视网膜静脉密度,碎形尺寸和 fundus 结构特征,如斑病变.
结论:
- 整合临床数据和CFP生物标志物的机器学习模型准确地预测了儿童的短期AL进展.
- 视网膜微血管和结构参数是近视AL延长的早期显著指标.
- 这种模型对儿童近视的早期检测和干预充满希望.
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