# Describing the Structural Phenotype of the Glaucomatous Optic Nerve Head Using Artificial Intelligence

arXiv: https://arxiv.org/abs/2012.09755 · HTML: https://mlsanity.com/paper/2012.09755/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 17 Dec 2020.

## Built on by (most cited first, 10 of 10)

- [Geometric Deep Learning to Identify the Critical 3D Structural Features of the Optic Nerve Head for Glaucoma Diagnosis](https://mlsanity.com/paper/2204.06931/) (2022)
- [The Three-Dimensional Structural Configuration of the Central Retinal Vessel Trunk and Branches as a Glaucoma Biomarker](https://mlsanity.com/paper/2111.03997/) (2021)
- [Medical Application of Geometric Deep Learning for the Diagnosis of Glaucoma](https://mlsanity.com/paper/2204.07004/) (2022)
- [Automatic Segmentation of the Optic Nerve Head Region in Optical Coherence Tomography: A Methodological Review](https://mlsanity.com/paper/2109.02322/) (2021)
- [3D Structural Analysis of the Optic Nerve Head to Robustly Discriminate Between Papilledema and Optic Disc Drusen](https://mlsanity.com/paper/2112.09970/) (2021)
- [AI-based Clinical Assessment of Optic Nerve Head Robustness Superseding Biomechanical Testing](https://mlsanity.com/paper/2206.04689/) (2022)
- [3D Structural Phenotype of the Optic Nerve Head at the Intersection of Glaucoma and Myopia -- A Key to Improving Glaucoma Diagnosis in Myopic Populations](https://mlsanity.com/paper/2503.19083/) (2025)
- [Enhanced Denoising of Optical Coherence Tomography Images Using Residual U-Net](https://mlsanity.com/paper/2407.13090/) (2024)
- [Trends, Challenges, and Future Directions in Deep Learning for Glaucoma: A Systematic Review](https://mlsanity.com/paper/2411.05876/) (2024)
- [Fusing Structural Phenotypes with Functional Data for Early Prediction of Primary Angle Closure Glaucoma Progression](https://mlsanity.com/paper/2508.14922/) (2025)

## Abstract (arXiv)

The optic nerve head (ONH) typically experiences complex neural- and connective-tissue structural changes with the development and progression of glaucoma, and monitoring these changes could be critical for improved diagnosis and prognosis in the glaucoma clinic. The gold-standard technique to assess structural changes of the ONH clinically is optical coherence tomography (OCT). However, OCT is limited to the measurement of a few hand-engineered parameters, such as the thickness of the retinal nerve fiber layer (RNFL), and has not yet been qualified as a stand-alone device for glaucoma diagnosis and prognosis applications. We argue this is because the vast amount of information available in a 3D OCT scan of the ONH has not been fully exploited. In this study we propose a deep learning approach that can: (1) fully exploit information from an OCT scan of the ONH; (2) describe the structural phenotype of the glaucomatous ONH; and that can (3) be used as a robust glaucoma diagnosis tool. Specifically, the structural features identified by our algorithm were found to be related to clinical observations of glaucoma. The diagnostic accuracy from these structural features was 92.0 ± 2.3 % with a sensitivity of 90.0 ± 2.4 %  (at 95 % specificity). By changing their magnitudes in steps, we were able to reveal how the morphology of the ONH changes as one transitions from a `non-glaucoma' to a `glaucoma' condition. We believe our work may have strong clinical implication for our understanding of glaucoma pathogenesis, and could be improved in the future to also predict future loss of vision.