人工智能将识别视神经头部的应变敏感区域,这些区域与玻璃眼的功能损失有关
Thanadet Chuangsuwanich1,2,3, Monisha E Nongpiur2,4, Fabian A Braeu2,4,5
1Yong Loo Lin School of Medicine, National University of Singapore, Singapore.
Investigative ophthalmology & visual science
|February 11, 2026
概括
通过组织应变测量视觉神经头部 (ONH) 生物力学,显著改善了与玻璃眼相关的视野损失模式的分类. 神经甲状腺边缘 神经甲状腺边缘
科学领域:
- 眼科和生物力学
- 医学成像和人工智能 医学成像和人工智能
背景情况:
- 玻璃眼是导致不可逆转失明的主要原因.
- 对视野损失模式的准确分类对于管理青光眼的进展至关重要.
- 目前使用的是视神经头部 (ONH) 形态学,但其对逐渐视野 (VF) 损失的预测能力有限.
研究的目的:
- 确定ONH生物力学,特别是组织菌株,是否有助于对青光眼中渐进性VF损失模式的分类.
- 通过使用可解释AI (XAI) 来确定对预测VF损失至关重要的特定ONH区域.
主要方法:
- 招募了249名青光瘤患者,在不同眼内压力 (IOP) 下对ONH进行成像.
- 使用数字体积相关性计算的神经组织和晶圆板 (LC) 菌株.
- 将生物机械和结构特征输入到PointNet模型中,用于VF缺陷分类.
- 利用突出地图 (XAI) 精确地确定容易受到应变的ONH区域.
主要成果:
- 纳入ONH菌株的模型实现了曲线下的面积 (AUC) 值从0.77到0.88.
- 添加菌株信息显著改善了上弧形缺陷的AUC,从0.83到0.87 (P <0.05).
- 在下部和下部的ONH边缘确定了易受应变的区域.
结论:
- ONH生物力学菌株显著提高了对眼性VF损失模式的分类准确性.
- 神经皮边缘,而不是LC,是最关键的区域,这表明它的应变是轴突损伤的关键.
- 这些发现强调了生物力学在理解和预测眼病进展方面的重要性.
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