结构表现型与功能数据的融合,用于早期预测初级视角闭眼的进展
Swati Sharma1, Thanadet Chuangsuwanich2, Royston K Y Tan1,3
1Singapore Eye Research Institute, Singapore National Eye Centre, Singapore.
ArXiv
|September 2, 2025
概括
这项研究开发了一种方法,将PACG患者分为进展缓慢或快速的患者. 结合视神经头部 (ONH) 结构和视野 (VF) 数据,提高了对青光眼进展的分类准确性.
科学领域:
- 眼科 眼科
- 医疗技术
- 医学中的人工智能
背景情况:
- 主要视角闭眼 (PACG) 是全球不可逆转失明的主要原因.
- 准确分类眼的进展对于及时干预和管理至关重要.
- 目前评估青光眼进展的方法往往缺乏整合结构和功能参数.
研究的目的:
- 开发和验证一种综合方法来将PACG患者的眼睛分类为缓慢或快速进展.
- 将视神经头部 (ONH) 的结构特征与基于部门的视野 (VF) 功能参数相结合,以改进分类.
- 使用机器学习识别青光眼进展的关键预测因素.
主要方法:
- 一项回顾性纵向研究包括至少5年随访的PACG患者和5个可靠的VF测试.
- 人工智能 (AI) 分段了OCT卷以提取31个ONH参数.
- 基于部门的VF数据 (每个地区的平均灵敏度) 与结构参数相结合,以训练机器学习 (ML) 分类器,并进行SHAP分析以识别预测因素.
主要成果:
- 这项研究分析了299名PACG患者的451只眼睛. 平均每年VFI的增长率为- 0. 92%.
- 一个整合结构和功能特征的随机森林模型实现了最高的分类性能 (AUC = 0.87±0.02).
- 关键预测因素包括较低的ONH特征 (MRW,RNFL厚度),鼻线曲率和较高的鼻线敏感度.
结论:
- 将ONH结构和VF功能参数结合起来,可以显著地提高在PACG中对眼症进展风险的分类.
- 较低的ONH特征,特别是MRW和RNFL厚度,被确定为最具预测性的参数.
- 这种综合方法突显了ONH形态在监测PACG疾病进展中的关键作用.
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