  Objective. (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, Setting and Participants. We
recruited 238 glaucoma subjects. 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.
Main Outcomes: 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). Conclusion: Our study
has shown that the integration of biomechanical data can significantly improve
the accuracy of visual field loss predictions. This highlights the importance
of the biomechanics-function relationship in glaucoma, and suggests that
biomechanics may serve as a crucial indicator for the development and
progression of glaucoma.
