3D Structural Phenotype of the Optic Nerve Head at the Intersection of Glaucoma and Myopia - A Key to Improving Glaucoma Diagnosis in Myopic Populations

• URL: http://arxiv.org/abs/2503.19083v1
• Date: Mon, 24 Mar 2025 19:14:22 GMT
• Title: 3D Structural Phenotype of the Optic Nerve Head at the Intersection of Glaucoma and Myopia - A Key to Improving Glaucoma Diagnosis in Myopic Populations
• Authors: Swati Sharma, Fabian A. Braeu, Thanadet Chuangsuwanich, Tin A. Tun, Quan V Hoang, Rachel Chong, Shamira Perera, Ching-Lin Ho, Rahat Husain, Martin L. Buist, Tin Aung, Michaël J. A. Girard,
• Abstract summary: To characterize the 3D structural phenotypes of the optic nerve head (ONH) in patients with glaucoma, high myopia, and concurrent high myopia and glaucoma.<n>To classify the 3D point clouds into four ONH conditions, i.e. H, HM, G, and HMG, a specialized ensemble network was developed.<n>The classification network achieved high accuracy, distinguishing H, HM, G, and HMG classes with a micro-average AUC of 0.92 $pm$ 0.03 on an independent test set.
• Score: 0.3818338427088651
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Purpose: To characterize the 3D structural phenotypes of the optic nerve head (ONH) in patients with glaucoma, high myopia, and concurrent high myopia and glaucoma, and to evaluate their variations across these conditions. Participants: A total of 685 optical coherence tomography (OCT) scans from 754 subjects of Singapore-Chinese ethnicity, including 256 healthy (H), 94 highly myopic (HM), 227 glaucomatous (G), and 108 highly myopic with glaucoma (HMG) cases. Methods: We segmented the retinal and connective tissues from OCT volumes and their boundary edges were converted into 3D point clouds. To classify the 3D point clouds into four ONH conditions, i.e., H, HM, G, and HMG, a specialized ensemble network was developed, consisting of an encoder to transform high-dimensional input data into a compressed latent vector, a decoder to reconstruct point clouds from the latent vector, and a classifier to categorize the point clouds into the four ONH conditions. Results: The classification network achieved high accuracy, distinguishing H, HM, G, and HMG classes with a micro-average AUC of 0.92 $\pm$ 0.03 on an independent test set. The decoder effectively reconstructed point clouds, achieving a Chamfer loss of 0.013 $\pm$ 0.002. Dimensionality reduction clustered ONHs into four distinct groups, revealing structural variations such as changes in retinal and connective tissue thickness, tilting and stretching of the disc and scleral canal opening, and alterations in optic cup morphology, including shallow or deep excavation, across the four conditions. Conclusions: This study demonstrated that ONHs exhibit distinct structural signatures across H, HM, G, and HMG conditions. The findings further indicate that ONH morphology provides sufficient information for classification into distinct clusters, with principal components capturing unique structural patterns within each group.

Related papers

• nnUNet RASPP for Retinal OCT Fluid Detection, Segmentation and Generalisation over Variations of Data Sources [25.095695898777656]
  We propose two variants of the nnUNet with consistent high performance across images from multiple device vendors. The algorithm was validated on the MICCAI 2017 RETOUCH challenge dataset. Experimental results show that our algorithms outperform the current state-of-the-arts algorithms.
  arXiv  Detail & Related papers  (2023-02-25T23:47:23Z)
• The 3D Structural Phenotype of the Glaucomatous Optic Nerve Head and its Relationship with The Severity of Visual Field Damage [45.923831389099696]
  We observed that the majority of ONH structural changes occurred in the early glaucoma stage, followed by a plateau effect in the later stages. Using PointNet, we also found that 3D ONH structural changes were present in both neural and connective tissues.
  arXiv  Detail & Related papers  (2023-01-07T12:28:43Z)
• Are Macula or Optic Nerve Head Structures better at Diagnosing Glaucoma? An Answer using AI and Wide-Field Optical Coherence Tomography [48.7576911714538]
  We developed a deep learning algorithm to automatically segment structures of the optic nerve head (ONH) and macula in 3D wide-field OCT scans. Our classification algorithm was able to segment ONH and macular tissues with a DC of 0.94 $pm$ 0.003. This may encourage the mainstream adoption of 3D wide-field OCT scans.
  arXiv  Detail & Related papers  (2022-10-13T01:51:29Z)
• AI-based Clinical Assessment of Optic Nerve Head Robustness Superseding Biomechanical Testing [54.306443917863355]
  We propose an AI-driven approach that can assess the robustness of a given ONH solely from a single OCT scan of the ONH. Longitudinal studies should establish whether ONH robustness could help us identify fast visual field loss progressors.
  arXiv  Detail & Related papers  (2022-06-09T11:29:28Z)
• Medical Application of Geometric Deep Learning for the Diagnosis of Glaucoma [60.42955087779866]
  3D scans of the optic nerve head (ONH) were acquired with Spectralis OCT for 477 glaucoma and 2,296 non-glaucoma subjects at the Singapore National Eye Centre. All volumes were automatically segmented using deep learning to identify 7 major neural and connective tissues. PointNet was able to provide a robust glaucoma diagnosis solely from the ONH represented as a 3D point cloud.
  arXiv  Detail & Related papers  (2022-04-14T14:55:25Z)
• Geometric Deep Learning to Identify the Critical 3D Structural Features of the Optic Nerve Head for Glaucoma Diagnosis [52.06403518904579]
  The optic nerve head (ONH) undergoes complex and deep 3D morphological changes during the development and progression of glaucoma. We used PointNet and dynamic graph convolutional neural network (DGCNN) to diagnose glaucoma from 3D ONH point clouds. Our approach may have strong potential to be used in clinical applications for the diagnosis and prognosis of a wide range of ophthalmic disorders.
  arXiv  Detail & Related papers  (2022-04-14T12:52:10Z)
• Describing the Structural Phenotype of the Glaucomatous Optic Nerve Head Using Artificial Intelligence [43.803153617553114]
  The optic nerve head (ONH) typically experiences complex neural- and connective-tissue structural changes with the development and progression of glaucoma. We propose a deep learning approach that can: textbf(1) fully exploit information from an OCT scan of the ONH; textbf(2) describe the structural phenotype of the glaucomatous ONH; and that can textbf (3) be used as a robust glaucoma diagnosis tool.
  arXiv  Detail & Related papers  (2020-12-17T17:15:30Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.