AI to Identify Strain-Sensitive Regions of the Optic Nerve Head Linked to Functional Loss in Glaucoma
Name
i1552-5783-67-2-29_1770803243.33602.pdf
Description
Published version
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3.14 MB
Format
Adobe PDF
Checksum (MD5)
e418484757e29957e43d737c94af4b6c
Author(s)
Chuangsuwanich, Thanadet
Nongpiur, Monisha E
Braeu, Fabian A
Prasad, Shimna Clara
Tun, Tin A
Thiéry, Alexandre
Perera, Shamira
Ho, Ching Lin
Buist, Martin
Barbastathis, George
Date Issued
February 11, 2026
Journal
Investigative Ophthalmology & Visual Science
Publisher
Association for Research in Vision and Ophthalmology (ARVO)
Citation
Thanadet Chuangsuwanich, Monisha E. Nongpiur, Fabian A. Braeu, Shimna Clara Prasad, Tin A. Tun, Alexandre Thiéry, Shamira Perera, Ching Lin Ho, Martin Buist, George Barbastathis, Tin Aung, Michaël J. A. Girard; AI to Identify Strain-Sensitive Regions of the Optic Nerve Head Linked to Functional Loss in Glaucoma. Invest. Ophthalmol. Vis. Sci. 2026;67(2):29.
Version
Final published version
Abstract
Purpose: The purposes of this study were to assess whether optic nerve head (ONH) biomechanics, quantified by tissue strain, improves classification of progressive visual field (VF) loss patterns in glaucoma beyond morphology, and to use saliency maps to identify ONH regions associated with the predictions.
Methods: We recruited 249 patients with glaucoma (mean age 69 ± 5 years, 54% female patients). One eye per subject was imaged under (1) primary gaze and (2) primary gaze with IOP elevated to approximately 35 millimeters of mercury (mm Hg) via ophthalmo-dynamometry. Twelve subjects were excluded due to poor scan quality/limited lamina cribrosa (LC) visibility. Experts classified subjects into four categories based on the presence of specific visual field defects (VFDs): (1) superior nasal step (N = 26), (2) superior partial arcuate (N = 62), (3) full superior hemifield defect (N = 25), and (4) other/non-specific defects (N = 124). Automatic segmentation and digital volume correlation computed neural tissue and LC strains. Biomechanical and structural features were input to a PointNet model. Three classification tasks were performed to detect: (1) superior nasal step, (2) superior partial arcuate, and (3) full superior hemifield defect. Data were split 80/20 (train/test). Area under the curve (AUC) assessed performance. Saliency maps (an explainable artificial intelligence [XAI] technique) highlighted ONH regions most critical to classification.
Results: Models achieved AUCs of 0.77 to 0.88 across VFD classifications. The structure-only model reached an AUC of 0.83 ± 0.02 for superior arcuate defects, which significantly improved to 0.87 ± 0.02 (P < 0.05) with the addition of strain information, demonstrating that ONH biomechanics enhance prediction beyond morphology. Strain-sensitive regions were localized to the inferior and inferotemporal rim, expanding with increasing severity of VF loss.
Conclusions: ONH strain enhances classification of glaucomatous VF loss patterns. The neuroretinal rim, rather than the LC, was most critical, suggesting rim strain may play a dominant role in axonal injury and functional loss.
MIT Department
Massachusetts Institute of Technology. Department of Mechanical Engineering
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Creative Commons Attribution-NonCommercial-NoDerivatives
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DOI of Published Version
https://doi.org/10.1167/iovs.67.2.29
