Geometric Deep Learning to Identify the Critical 3D Structural Features of the Optic Nerve Head for Glaucoma Diagnosis

• URL: http://arxiv.org/abs/2204.06931v1
• Date: Thu, 14 Apr 2022 12:52:10 GMT
• Title: Geometric Deep Learning to Identify the Critical 3D Structural Features of the Optic Nerve Head for Glaucoma Diagnosis
• Authors: Fabian A. Braeu, Alexandre H. Thi\'ery, Tin A. Tun, Aiste Kadziauskiene, George Barbastathis, Tin Aung, and Micha\"el J.A. Girard
• Abstract summary: 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.
• Score: 52.06403518904579
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Purpose: The optic nerve head (ONH) undergoes complex and deep 3D morphological changes during the development and progression of glaucoma. Optical coherence tomography (OCT) is the current gold standard to visualize and quantify these changes, however the resulting 3D deep-tissue information has not yet been fully exploited for the diagnosis and prognosis of glaucoma. To this end, we aimed: (1) To compare the performance of two relatively recent geometric deep learning techniques in diagnosing glaucoma from a single OCT scan of the ONH; and (2) To identify the 3D structural features of the ONH that are critical for the diagnosis of glaucoma. Methods: In this study, we included a total of 2,247 non-glaucoma and 2,259 glaucoma scans from 1,725 subjects. All subjects had their ONHs imaged in 3D with Spectralis OCT. All OCT scans were automatically segmented using deep learning to identify major neural and connective tissues. Each ONH was then represented as a 3D point cloud. We used PointNet and dynamic graph convolutional neural network (DGCNN) to diagnose glaucoma from such 3D ONH point clouds and to identify the critical 3D structural features of the ONH for glaucoma diagnosis. Results: Both the DGCNN (AUC: 0.97$\pm$0.01) and PointNet (AUC: 0.95$\pm$0.02) were able to accurately detect glaucoma from 3D ONH point clouds. The critical points formed an hourglass pattern with most of them located in the inferior and superior quadrant of the ONH. Discussion: The diagnostic accuracy of both geometric deep learning approaches was excellent. Moreover, we were able to identify the critical 3D structural features of the ONH for glaucoma diagnosis that tremendously improved the transparency and interpretability of our method. Consequently, our approach may have strong potential to be used in clinical applications for the diagnosis and prognosis of a wide range of ophthalmic disorders.

Related papers

• 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)
• The Three-Dimensional Structural Configuration of the Central Retinal Vessel Trunk and Branches as a Glaucoma Biomarker [41.97805846007449]
  We trained a deep learning network to automatically segment the CRVT&B from the B-scans of the optical coherence tomography volume of the optic nerve head (ONH) The 3D and 2D diagnostic networks were able to differentiate glaucoma from non-glaucoma subjects with accuracies of 82.7% and 83.3%, respectively.
  arXiv  Detail & Related papers  (2021-11-07T04:41:49Z)
• 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.