基于人工智能的多模模式对青光眼的进展进行建模:3PM指导的方法整合了结构,功能和血管模式
Natalia I Kurysheva1, Oxana Ye Rodionova2, Alexey L Pomerantsev2
1The Ophthalmological Center of the Federal Medical and Biological Agency of the Russian Federation, 15 Gamalei Street, Moscow, 123098 Russian Federation.
The EPMA journal
|November 28, 2025
概括
这项研究开发了一种个性化的模型,使用多式生物标志物预测初级开角青光眼 (POAG) 的进展. 这种方法有助于分层患者进行量身定制的治疗,并预防不可逆转的失明.
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:
- 玻璃眼是全球不可逆转失明的主要原因.
- 青光眼进展的高个体间变异性需要预测,预防和个性化医疗 (3PM) 策略.
- 有效的患者分层对于保护个人免受疾病进展至关重要.
研究的目的:
- 开发和验证一个个性化的,多式预测模型框架,用于初级开角青光眼 (POAG).
- 整合结构性,功能性和血管生物标志物,以实现POAG进展率的个性化风险分层.
- 通过精确预测疾病轨迹来加强患者管理.
主要方法:
- 在不同阶段对POAG患者进行长期监测 (≥36个月).
- 综合多模式数据收集:光学连贯断层扫描 (OCT),OCT血管图 (OCT-A),自动周边测量,生物机械评估.
- 使用排列部分最小平方差分分析 (排列PLS-DA) 与Procrustes交叉验证进行预测建模.
主要成果:
- 开发了多达27个早期POAG参数和20个高级POAG参数的模型,实现了高预测准确性 (AUC高达0.90).
- 确定了根据疾病阶段不同的主要预测生物标志物:RNFL厚度,微血管脱落,血管密度和早期POAG中的角膜歇斯底里;年龄,晚期POAG中的质细胞复合体厚度,黄斑厚度和 perfusion 参数.
- 证明了该框架能够分类缓慢,中度和快速的玻璃眼病进展率的能力.
结论:
- 多式预测建模框架使准确的风险分层和个性化的玻璃眼病管理成为可能.
- 临床应用包括初始分析,定期重新校准和适应性治疗策略,以改善视觉结果.
- 这种3PM方法优化了资源利用,并增强了患者对疾病进展的保护.
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