Are Macula or Optic Nerve Head Structures better at Diagnosing Glaucoma? An Answer using AI and Wide-Field Optical Coherence Tomography

• URL: http://arxiv.org/abs/2210.06664v1
• Date: Thu, 13 Oct 2022 01:51:29 GMT
• Title: Are Macula or Optic Nerve Head Structures better at Diagnosing Glaucoma? An Answer using AI and Wide-Field Optical Coherence Tomography
• Authors: Charis Y.N. Chiang, Fabian Braeu, Thanadet Chuangsuwanich, Royston K.Y. Tan, Jacqueline Chua, Leopold Schmetterer, Alexandre Thiery, Martin Buist, Micha\"el J.A. Girard
• Abstract summary: 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.
• Score: 48.7576911714538
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Purpose: (1) To develop a deep learning algorithm to automatically segment structures of the optic nerve head (ONH) and macula in 3D wide-field optical coherence tomography (OCT) scans; (2) To assess whether 3D macula or ONH structures (or the combination of both) provide the best diagnostic power for glaucoma. Methods: A cross-sectional comparative study was performed which included wide-field swept-source OCT scans from 319 glaucoma subjects and 298 non-glaucoma subjects. All scans were compensated to improve deep-tissue visibility. We developed a deep learning algorithm to automatically label all major ONH tissue structures by using 270 manually annotated B-scans for training. The performance of our algorithm was assessed using the Dice coefficient (DC). A glaucoma classification algorithm (3D CNN) was then designed using a combination of 500 OCT volumes and their corresponding automatically segmented masks. This algorithm was trained and tested on 3 datasets: OCT scans cropped to contain the macular tissues only, those to contain the ONH tissues only, and the full wide-field OCT scans. The classification performance for each dataset was reported using the AUC. Results: Our segmentation algorithm was able to segment ONH and macular tissues with a DC of 0.94 $\pm$ 0.003. The classification algorithm was best able to diagnose glaucoma using wide-field 3D-OCT volumes with an AUC of 0.99 $\pm$ 0.01, followed by ONH volumes with an AUC of 0.93 $\pm$ 0.06, and finally macular volumes with an AUC of 0.91 $\pm$ 0.11. Conclusions: this study showed that using wide-field OCT as compared to the typical OCT images containing just the ONH or macular may allow for a significantly improved glaucoma diagnosis. This may encourage the mainstream adoption of 3D wide-field OCT scans. For clinical AI studies that use traditional machines, we would recommend the use of ONH scans as opposed to macula scans.

Related papers

• 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)
• 3D Structural Analysis of the Optic Nerve Head to Robustly Discriminate Between Papilledema and Optic Disc Drusen [44.754910718620295]
  We developed a deep learning algorithm to identify major tissue structures of the optic nerve head (ONH) in 3D optical coherence tomography ( OCT) scans. A classification algorithm was designed using 150 OCT volumes to perform 3-class classifications (1: ODD, 2: papilledema, 3: healthy) strictly from their drusen and prelamina swelling scores. Our AI approach accurately discriminated ODD from papilledema, using a single OCT scan.
  arXiv  Detail & Related papers  (2021-12-18T17:05:53Z)
• LIFE: A Generalizable Autodidactic Pipeline for 3D OCT-A Vessel Segmentation [5.457168581192045]
  Recent deep learning algorithms produced promising vascular segmentation results. However, 3D retinal vessel segmentation remains difficult due to the lack of manually annotated training data. We propose a learning-based method that is only supervised by a self-synthesized modality.
  arXiv  Detail & Related papers  (2021-07-09T07:51:33Z)
• A Global Benchmark of Algorithms for Segmenting Late Gadolinium-Enhanced Cardiac Magnetic Resonance Imaging [90.29017019187282]
  " 2018 Left Atrium Challenge" using 154 3D LGE-MRIs, currently the world's largest cardiac LGE-MRI dataset. Analyse of the submitted algorithms using technical and biological metrics was performed. Results show the top method achieved a dice score of 93.2% and a mean surface to a surface distance of 0.7 mm.
  arXiv  Detail & Related papers  (2020-04-26T08:49:17Z)

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.