PURPOSE (1) To assess whether neural tissue structure and biomechanics could predict functional loss in glaucoma; (2) To evaluate the importance of biomechanics in making such predictions. DESIGN Clinic-based cross-sectional study. METHODS We recruited 238 glaucoma subjects (Chinese ethnicity, more than 50 years old). For one eye of each subject, we imaged the optic nerve head (ONH) using spectral-domain OCT under the following conditions: (1) primary gaze and (2) primary gaze with acute IOP elevation (to approximately 35 mmHg) achieved through ophthalmo-dynamometry. We utilized automatic segmentation of optic nerve head (ONH) tissues and digital volume correlation (DVC) analysis to compute intraocular pressure (IOP)-induced neural tissue strains. A robust geometric deep learning approach, known as Point-Net, was employed to predict the full Humphrey 24-2 pattern standard deviation (PSD) maps from ONH structural and biomechanical information. For each point in each PSD map, we predicted whether it exhibited no defect or a PSD value of less than 5%. Predictive performance was evaluated using 5-fold cross-validation and the F1-score. We compared the model's performance with and without the inclusion of IOP-induced strains to assess the impact of biomechanics on prediction accuracy. RESULTS Integrating biomechanical (IOP-induced neural tissue strains) and structural (tissue morphology and neural tissues thickness) information yielded a significantly better predictive model (F1-score: 0.76 ± 0.02) across validation subjects, as opposed to relying only on structural information, which resulted in a significantly lower F1-score of 0.71 ± 0.02 (p < 0.05). Our subjects had a mean age of 69±5 years. Among them, 88 were female. The cohort included a wide range of glaucoma severity, with Mean Deviation (MD) values ranging from -1.8 (mild) to -25.2 (severe), and an average MD value of -7.25±5.05. CONCLUSION Our study has shown that the integration of biomechanical data can significantly improve the accuracy of visual field loss predictions and highlights the importance of the biomechanics-function relationship in glaucoma.

中文翻译：

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青光眼的生物力学功能：从 IOP 诱导的神经应变中改进视野预测。


目的 （1） 评估神经组织结构和生物力学是否可以预测青光眼的功能丧失;（2） 评估生物力学在做出此类预测中的重要性。设计 基于诊所的横断面研究。方法 我们招募了 238 例青光眼受试者 （华裔，年龄超过 50 岁）。对于每个受试者的一只眼睛，我们在以下条件下使用光谱域 OCT 对视神经头 （ONH） 进行成像：（1） 初级凝视和 （2） 通过眼测力计实现急性 IOP 升高（至约 35 mmHg）的初级凝视。我们利用视神经头 （ONH） 组织的自动分割和数字体积相关性 （DVC） 分析来计算眼压 （IOP） 诱导的神经组织应变。采用一种强大的几何深度学习方法，称为 Point-Net，从 ONH 结构和生物力学信息中预测完整的汉弗莱 24-2 模式标准差 （PSD） 图。对于每个 PSD 图中的每个点，我们预测了它是没有缺陷还是 PSD 值小于 5%。使用 5 倍交叉验证和 F1 评分评估预测性能。我们比较了模型在包含 IOP 诱导菌株和不包含 IOP 诱导菌株的情况下的性能，以评估生物力学对预测准确性的影响。结果整合生物力学 （IOP 诱导的神经组织应变） 和结构 （组织形态和神经组织厚度） 信息在验证对象中产生了明显更好的预测模型 （F1 评分： 0.76 ± 0.02），而不是仅依赖结构信息，这导致 F1 评分显着降低 0.71 ± 0.02 （p < 0.05）。我们的受试者平均年龄为 69±5 岁。其中，88 名女性。 该队列包括广泛的青光眼严重程度，平均偏差 （MD） 值从 -1.8（轻度）到 -25.2（严重）不等，平均 MD 值为 -7.25±5.05。结论 我们的研究表明，生物力学数据的整合可以显著提高视野损失预测的准确性，并突出了生物力学-功能关系在青光眼中的重要性。