24-2视野模式的模糊聚类可以检测青光眼的进展
Hwayeong Kim1, Sangwoo Moon2, Joohwang Lee1
1Department of Ophthalmology, Pusan National University College of Medicine, Busan, Korea.
PloS one
|September 4, 2024
概括
本研究引入了混合原型分析 (AA) 和模糊c-means (FCM) 聚类方法来分析视野 (VF) 模式. 该方法增强了VF进展的检测,提供了更精确的视力损失的理解.
科学领域:
- 眼科医生 眼科 眼科
- 数据科学数据科学数据科学
- 医疗成像医学成像
背景情况:
- 视野 (VF) 损失是青光眼和其他神经疾病的关键指标.
- 准确表示和预测VF变化对于患者管理至关重要.
- 目前的方法可能缺乏细粒度,以有效地捕获单个VF模式.
研究的目的:
- 开发和验证混合原型分析 (AA) 和模糊c-means (FCM) 聚类方法,用于分析24-2视野 (VF) 数据.
- 使用特征模式表示个体患者的VF损失.
- 为了提高检测和预测VF的进展.
主要方法:
- 一个多中心的回顾性研究,使用了来自18033名参与者的132,938个VF.
- 使用AA对24-2VF模式进行分类,然后通过FCM集群进行分解.
- 使用监督机器学习来预测从分解系数中平均偏差 (MD) 的变化.
- 使用不同标准检测VF进展的接收器操作特征曲线 (AUC) 下的区域比较.
主要成果:
- 使用AA.识别了16个特征性VF模式 (原型) 的识别.
- 混合AA-FCM方法在预测MD变化方面表现优异,与单独AA相比 (较低的平均平方误差,更高的相关系数).
- 与AA相比,FCM分解系数的斜率显示了对特定缺陷的VF进展的更好的检测 (上部高度,双弧度,总损失).
结论:
- 一种混合AA-FCM方法有效地将24-2 VF测试可视化为特征模式.
- 这种方法增强了检测VF进展的无损分解.
- 这些发现表明,在临床实践中理解和监测视力丧失是一个有价值的工具.
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