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视野预测使用K-means集群在患有原发性开角玻璃眼患者

Junyoung Lee¹, Jihun Kim², Hwayoung Kim¹

¹Department of Ophthalmology, Pusan National University College of Medicine, Busan 49241, Republic of Korea.

International journal of ophthalmology

|January 12, 2026

概括

K-means 聚类准确地预测了初级开角玻璃眼患者的长期视野测试结果. 这种方法在有限的视野数据下提供了更高的准确性,有助于疾病管理.

科学领域:

眼科医生眼科眼科
医疗成像医学成像
数据科学数据科学数据科学

背景情况:

主要开角光眼 (POAG) 是不可逆转失明的主要原因.
准确的长期预测视野 (VF) 进展对于管理POAG至关重要.
目前的预测方法可能有局限性,尤其是稀疏数据.

研究的目的:

评估K-means聚类对预测POAG患者长期视野结果的有效性.
为了比较K-means集群的预测准确度与其他方法,如等级有序分区和崩混合 (HOPACH) 和点向线性回归 (PLR).

主要方法:

利用了228名POAG患者 (培训) 和81名患者 (测试) 的数据,至少进行了10次视野测试 (24-2 VF).
应用K-平均集群和HOPACH集群52个从初始VF测试中总偏差值 (TDVs).
在聚类区域上使用线性回归来预测第10个VF测试的TDV,比较预测误差 (RMSE).

主要成果:

K-means集群确定了九个不同的VF区域,而HOPACH确定了十一个.
对于1:3和1:4的预测比率,K-means集群显示预测误差明显低于PLR.
在各种比率的预测准确性方面,K-means集群通常优于HOPACH,除了1:3.

结论:

K-means集群提供了一个更准确的方法来预测POAG患者的长期视野测试结果.
这种聚类方法在处理有限的视野数据时尤为有益.
K-means 集群显示为增强绿眼病管理策略的有价值工具.

关键词:

K-表示集群.层次上有序的分区和崩的混合动力.一个点向直线回归的点向直线回归.视野预测视野预测

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