Introducing the Biomechanics-Function Relationship in Glaucoma: Improved Visual Field Loss Predictions from intraocular pressure-induced Neural Tissue Strains
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| Main Authors: | , , , , , , , , , , |
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| Format: | Preprint |
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2024
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| _version_ | 1866916296415772672 |
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| author | 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. |
| author_facet | 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. |
| contents | 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. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2406_14988 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Introducing the Biomechanics-Function Relationship in Glaucoma: Improved Visual Field Loss Predictions from intraocular pressure-induced Neural Tissue Strains 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. Image and Video Processing Artificial Intelligence 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. |
| title | Introducing the Biomechanics-Function Relationship in Glaucoma: Improved Visual Field Loss Predictions from intraocular pressure-induced Neural Tissue Strains |
| topic | Image and Video Processing Artificial Intelligence |
| url | https://arxiv.org/abs/2406.14988 |