AI to Identify Strain-sensitive Regions of the Optic Nerve Head Linked to Functional Loss in Glaucoma

• URL: http://arxiv.org/abs/2506.17262v1
• Date: Mon, 09 Jun 2025 16:00:01 GMT
• Title: AI to Identify Strain-sensitive Regions of the Optic Nerve Head Linked to Functional Loss in Glaucoma
• Authors: Thanadet Chuangsuwanich, Monisha E. Nongpiur, Fabian A. Braeu, Tin A. Tun, Alexandre Thiery, Shamira Perera, Ching Lin Ho, Martin Buist, George Barbastathis, Tin Aung, Michaël J. A. Girard,
• Abstract summary: The ONH of one eye was imaged under two conditions: (1) primary gaze and (2) primary gaze with IOP elevated to 35 mmHg.<n>The inferior and inferotemporal rim were identified as key strain-sensitive regions, contributing most to visual field loss prediction.
• Score: 31.874825130479174
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Objective: (1) To assess whether ONH biomechanics improves prediction of three progressive visual field loss patterns in glaucoma; (2) to use explainable AI to identify strain-sensitive ONH regions contributing to these predictions. Methods: We recruited 237 glaucoma subjects. The ONH of one eye was imaged under two conditions: (1) primary gaze and (2) primary gaze with IOP elevated to ~35 mmHg via ophthalmo-dynamometry. Glaucoma experts classified the subjects into four categories based on the presence of specific visual field defects: (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 ONH tissue segmentation and digital volume correlation were used to compute IOP-induced neural tissue and lamina cribrosa (LC) strains. Biomechanical and structural features were input to a Geometric Deep Learning model. Three classification tasks were performed to detect: (1) superior nasal step, (2) superior partial arcuate, (3) full superior hemifield defect. For each task, the data were split into 80% training and 20% testing sets. Area under the curve (AUC) was used to assess performance. Explainable AI techniques were employed to highlight the ONH regions most critical to each classification. Results: Models achieved high AUCs of 0.77-0.88, showing that ONH strain improved VF loss prediction beyond morphology alone. The inferior and inferotemporal rim were identified as key strain-sensitive regions, contributing most to visual field loss prediction and showing progressive expansion with increasing disease severity. Conclusion and Relevance: ONH strain enhances prediction of glaucomatous VF loss patterns. Neuroretinal rim, rather than the LC, was the most critical region contributing to model predictions.

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