Literature DB >> 32826205

Estimating Global Visual Field Indices in Glaucoma by Combining Macula and Optic Disc OCT Scans Using 3-Dimensional Convolutional Neural Networks.

Hsin-Hao Yu1, Stefan R Maetschke2, Bhavna J Antony2, Hiroshi Ishikawa3, Gadi Wollstein3, Joel S Schuman4, Rahil Garnavi2.   

Abstract

PURPOSE: To evaluate the accuracy at which visual field global indices could be estimated from OCT scans of the retina using deep neural networks and to quantify the contributions to the estimates by the macula (MAC) and the optic nerve head (ONH).
DESIGN: Observational cohort study. PARTICIPANTS: A total of 10 370 eyes from 109 healthy patients, 697 glaucoma suspects, and 872 patients with glaucoma over multiple visits (median = 3).
METHODS: Three-dimensional convolutional neural networks were trained to estimate global visual field indices derived from automated Humphrey perimetry (SITA 24-2) tests (Zeiss, Dublin, CA), using OCT scans centered on MAC, ONH, or both (MAC + ONH) as inputs. MAIN OUTCOME MEASURES: Spearman's rank correlation coefficients, Pearson's correlation coefficient, and absolute errors calculated for 2 indices: visual field index (VFI) and mean deviation (MD).
RESULTS: The MAC + ONH achieved 0.76 Spearman's correlation coefficient and 0.87 Pearson's correlation for VFI and MD. Median absolute error was 2.7 for VFI and 1.57 decibels (dB) for MD. Separate MAC or ONH estimates were significantly less correlated and less accurate. Accuracy was dependent on the OCT signal strength and the stage of glaucoma severity.
CONCLUSIONS: The accuracy of global visual field indices estimate is improved by integrating information from MAC and ONH in advanced glaucoma, suggesting that structural changes of the 2 regions have different time courses in the disease severity spectrum.
Copyright © 2020 American Academy of Ophthalmology. Published by Elsevier Inc. All rights reserved.

Entities:  

Keywords:  AI; OCT; glaucoma; machine learning; structure-function relationship; visual field

Mesh:

Year:  2020        PMID: 32826205      PMCID: PMC7855239          DOI: 10.1016/j.ogla.2020.07.002

Source DB:  PubMed          Journal:  Ophthalmol Glaucoma        ISSN: 2589-4196


  25 in total

1.  Structure-function relationships using the Cirrus spectral domain optical coherence tomograph and standard automated perimetry.

Authors:  Mauro T Leite; Linda M Zangwill; Robert N Weinreb; Harsha L Rao; Luciana M Alencar; Felipe A Medeiros
Journal:  J Glaucoma       Date:  2012-01       Impact factor: 2.503

2.  Relationship between visual field sensitivity and retinal nerve fiber layer thickness as measured by optical coherence tomography.

Authors:  Csilla Ajtony; Zsolt Balla; Szabolcs Somoskeoy; Balint Kovacs
Journal:  Invest Ophthalmol Vis Sci       Date:  2007-01       Impact factor: 4.799

3.  Structure-function relationships using confocal scanning laser ophthalmoscopy, optical coherence tomography, and scanning laser polarimetry.

Authors:  Christopher Bowd; Linda M Zangwill; Felipe A Medeiros; Ivan M Tavares; Esther M Hoffmann; Rupert R Bourne; Pamela A Sample; Robert N Weinreb
Journal:  Invest Ophthalmol Vis Sci       Date:  2006-07       Impact factor: 4.799

4.  Structure versus function in glaucoma: an application of a linear model.

Authors:  Donald C Hood; Susan C Anderson; Michael Wall; Randy H Kardon
Journal:  Invest Ophthalmol Vis Sci       Date:  2007-08       Impact factor: 4.799

5.  Detecting glaucoma with visual fields derived from frequency-domain optical coherence tomography.

Authors:  Xian Zhang; Ali S Raza; Donald C Hood
Journal:  Invest Ophthalmol Vis Sci       Date:  2013-05-07       Impact factor: 4.799

Review 6.  Improving our understanding, and detection, of glaucomatous damage: An approach based upon optical coherence tomography (OCT).

Authors:  Donald C Hood
Journal:  Prog Retin Eye Res       Date:  2016-12-22       Impact factor: 21.198

Review 7.  The Evolving Role of the Relationship between Optic Nerve Structure and Function in Glaucoma.

Authors:  Jithin Yohannan; Michael V Boland
Journal:  Ophthalmology       Date:  2017-12       Impact factor: 12.079

8.  A feature agnostic approach for glaucoma detection in OCT volumes.

Authors:  Stefan Maetschke; Bhavna Antony; Hiroshi Ishikawa; Gadi Wollstein; Joel Schuman; Rahil Garnavi
Journal:  PLoS One       Date:  2019-07-01       Impact factor: 3.240

9.  Structural Change Can Be Detected in Advanced-Glaucoma Eyes.

Authors:  Akram Belghith; Felipe A Medeiros; Christopher Bowd; Jeffrey M Liebmann; Christopher A Girkin; Robert N Weinreb; Linda M Zangwill
Journal:  Invest Ophthalmol Vis Sci       Date:  2016-07-01       Impact factor: 4.799

10.  Artificial Intelligence Mapping of Structure to Function in Glaucoma.

Authors:  Eduardo B Mariottoni; Shounak Datta; David Dov; Alessandro A Jammal; Samuel I Berchuck; Ivan M Tavares; Lawrence Carin; Felipe A Medeiros
Journal:  Transl Vis Sci Technol       Date:  2020-03-30       Impact factor: 3.283

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  11 in total

1.  Artificial Intelligence for Glaucoma: Creating and Implementing Artificial Intelligence for Disease Detection and Progression.

Authors:  Lama A Al-Aswad; Rithambara Ramachandran; Joel S Schuman; Felipe Medeiros; Malvina B Eydelman
Journal:  Ophthalmol Glaucoma       Date:  2022-02-24

2.  Policy-Driven, Multimodal Deep Learning for Predicting Visual Fields from the Optic Disc and OCT Imaging.

Authors:  Yuka Kihara; Giovanni Montesano; Andrew Chen; Nishani Amerasinghe; Chrysostomos Dimitriou; Aby Jacob; Almira Chabi; David P Crabb; Aaron Y Lee
Journal:  Ophthalmology       Date:  2022-02-21       Impact factor: 14.277

3.  Association between visual field damage and corneal structural parameters.

Authors:  Alexandru Lavric; Valentin Popa; Hidenori Takahashi; Rossen M Hazarbassanov; Siamak Yousefi
Journal:  Sci Rep       Date:  2021-05-24       Impact factor: 4.379

4.  Visual Field Inference From Optical Coherence Tomography Using Deep Learning Algorithms: A Comparison Between Devices.

Authors:  Jonghoon Shin; Sungjoon Kim; Jinmi Kim; Keunheung Park
Journal:  Transl Vis Sci Technol       Date:  2021-06-01       Impact factor: 3.283

5.  Estimating the Severity of Visual Field Damage From Retinal Nerve Fiber Layer Thickness Measurements With Artificial Intelligence.

Authors:  Xiaoqin Huang; Jian Sun; Juleke Majoor; Koenraad Arndt Vermeer; Hans Lemij; Tobias Elze; Mengyu Wang; Michael Vincent Boland; Louis Robert Pasquale; Vahid Mohammadzadeh; Kouros Nouri-Mahdavi; Chris Johnson; Siamak Yousefi
Journal:  Transl Vis Sci Technol       Date:  2021-08-02       Impact factor: 3.283

6.  A Case for the Use of Artificial Intelligence in Glaucoma Assessment.

Authors:  Joel S Schuman; Maria De Los Angeles Ramos Cadena; Rebecca McGee; Lama A Al-Aswad; Felipe A Medeiros
Journal:  Ophthalmol Glaucoma       Date:  2021-12-22

7.  Identifying the Retinal Layers Linked to Human Contrast Sensitivity Via Deep Learning.

Authors:  Foroogh Shamsi; Rong Liu; Cynthia Owsley; MiYoung Kwon
Journal:  Invest Ophthalmol Vis Sci       Date:  2022-02-01       Impact factor: 4.799

8.  Pointwise Visual Field Estimation From Optical Coherence Tomography in Glaucoma Using Deep Learning.

Authors:  Ruben Hemelings; Bart Elen; João Barbosa-Breda; Erwin Bellon; Matthew B Blaschko; Patrick De Boever; Ingeborg Stalmans
Journal:  Transl Vis Sci Technol       Date:  2022-08-01       Impact factor: 3.048

Review 9.  The Development and Clinical Application of Innovative Optical Ophthalmic Imaging Techniques.

Authors:  Palaiologos Alexopoulos; Chisom Madu; Gadi Wollstein; Joel S Schuman
Journal:  Front Med (Lausanne)       Date:  2022-06-30

10.  RetiNerveNet: using recursive deep learning to estimate pointwise 24-2 visual field data based on retinal structure.

Authors:  Shounak Datta; Eduardo B Mariottoni; David Dov; Alessandro A Jammal; Lawrence Carin; Felipe A Medeiros
Journal:  Sci Rep       Date:  2021-06-15       Impact factor: 4.379

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