Biomechanics-Function in Glaucoma: Improved Visual Field Predictions from IOP-Induced Neural Strains

• Published in: American journal of ophthalmology 2024-12
• Main Authors: Chuangsuwanich, Thanadet, Nongpiur, Monisha E., Braeu, Fabian A., Tun, Tin A., Thiery, Alexandre, Perera, Shamira, Ho, Ching Lin, Buist, Martin, Barbastathis, George, Aung, Tin, Girard, Michaël J.A.
• Format: Article
• Language: English
• ISSN: 0002-9394; 1879-1891; 1879-1891
• DOI: 10.1016/j.ajo.2024.11.019

(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. Clinic-based cross-sectional study. 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. 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. 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.