Online citations, reference lists, and bibliographies.
← Back to Search

Artificial Intelligence Classification Of Central Visual Field Patterns In Glaucoma.

Mengyu Wang, L. Shen, L. Pasquale, Michael V. Boland, S. Wellik, C. G. De Moraes, J. Myers, T. Nguyen, R. Ritch, P. Ramulu, H. Wang, J. Tichelaar, Dian Li, P. Bex, Tobias Elze
Published 2019 · Medicine

Cite This
Download PDF
Analyze on Scholarcy
PURPOSE To quantify the central visual field (VF) loss patterns in glaucoma using artificial intelligence. DESIGN Retrospective study. PARTICIPANTS VFs of 8712 patients with 13 951 Humphrey 10-2 test results from 13 951 eyes for cross-sectional analyses, and 824 patients with at least 5 reliable 10-2 test results at 6-month intervals or more from 1191 eyes for longitudinal analyses. METHODS Total deviation values were used to determine the central VF patterns using the most recent 10-2 test results. A 24-2 VF within a 3-month window of the 10-2 tests was used to stage eyes into mild, moderate, or severe functional loss using the Hodapp-Anderson-Parrish scale at baseline. Archetypal analysis was applied to determine the central VF patterns. Cross-validation was performed to determine the optimal number of patterns. Stepwise regression was applied to select the optimal feature combination of global indices, average baseline decomposition coefficients from central VFs archetypes, and other factors to predict central VF mean deviation (MD) slope based on the Bayesian information criterion (BIC). MAIN OUTCOME MEASURES The central VF patterns stratified by severity stage based on 24-2 test results and a model to predict the central VF MD change over time using baseline test results. RESULTS From cross-sectional analysis, 17 distinct central VF patterns were determined for the 13 951 eyes across the spectrum of disease severity. These central VF patterns could be divided into isolated superior loss, isolated inferior loss, diffuse loss, and other loss patterns. Notably, 4 of the 5 patterns of diffuse VF loss preserved the less vulnerable inferotemporal zone, whereas they lost most of the remaining more vulnerable zone described by the Hood model. Inclusion of coefficients from central VF archetypical patterns strongly improved the prediction of central VF MD slope (BIC decrease, 35; BIC decrease of >6 indicating strong prediction improvement) than using only the global indices of 2 baseline VF results. Eyes with baseline VF results with more superonasal and inferonasal loss were more likely to show worsening MD over time. CONCLUSIONS We quantified central VF patterns in glaucoma, which were used to improve the prediction of central VF worsening compared with using only global indices.
This paper references
Assessment of false positives with the Humphrey Field Analyzer II perimeter with the SITA Algorithm.
Michelle R. Newkirk (2006)
Application of advanced statistics in ophthalmology.
Qiao Fan (2011)
An Artificial Intelligence Approach to Detect Visual Field Progression in Glaucoma Based on Spatial Pattern Analysis
Mengyu Wang (2019)
Prevalence and nature of early glaucomatous defects in the central 10° of the visual field.
Ilana Traynis (2014)
Using unsupervised learning with independent component analysis to identify patterns of glaucomatous visual field defects.
M. Goldbaum (2005)
The effect of trabeculectomy surgery on the central visual field in patients with glaucoma using microperimetry and optical coherence tomography
G. Ratnarajan (2018)
[Reading performance in patients with central visual field disturbance due to glaucoma].
K. Fujita (2006)
Finding Glaucoma in Color Fundus Photographs Using Deep Learning.
K. D. Bojikian (2019)
Driving Performance of Glaucoma Patients Correlates With Peripheral Visual Field Loss
J. Szlyk (2005)
Anthropometric measures and their relation to incident primary open-angle glaucoma.
L. Pasquale (2010)
Assessing visual fields for driving in patients with paracentral scotomata
C. Chisholm (2008)
False-negative responses in glaucoma perimetry: indicators of patient performance or test reliability?
Bengtsson (2000)
Identifying Areas of the Visual Field Important for Quality of Life in Patients with Glaucoma
H. Murata (2013)
Initial arcuate defects within the central 10 degrees in glaucoma.
Donald C. Hood (2011)
Reversal of Glaucoma Hemifield Test Results and Visual Field Features in Glaucoma.
Mengyu Wang (2018)
Keratoconus severity identification using unsupervised machine learning
S. Yousefi (2018)
Risk factors for progression of visual field abnormalities in normal-tension glaucoma.
S. Drance (2001)
Unsupervised Gaussian Mixture-Model With Expectation Maximization for Detecting Glaucomatous Progression in Standard Automated Perimetry Visual Fields
S. Yousefi (2016)
Estimating the Dimension of a Model
G. Schwarz (1978)
Principal component analysis
S. Wold (1987)
A k-means clustering algorithm
J. Hartigan (1979)
Patterns of functional vision loss in glaucoma determined with archetypal analysis
Tobias Elze (2015)
Clinical Correlates of Computationally Derived Visual Field Defect Archetypes in Patients from a Glaucoma Clinic
S. Cai (2017)
Detecting Preperimetric Glaucoma with Standard Automated Perimetry Using a Deep Learning Classifier.
R. Asaoka (2016)
Forecasting future Humphrey Visual Fields using deep learning
J. Wen (2019)
Using Deep Learning and Transfer Learning to Accurately Diagnose Early-Onset Glaucoma From Macular Optical Coherence Tomography Images.
R. Asaoka (2019)
Prospective study of type 2 diabetes mellitus and risk of primary open-angle glaucoma in women.
L. Pasquale (2006)
Retinal nerve fiber layer imaging with spectral-domain optical coherence tomography: patterns of retinal nerve fiber layer progression.
C. Leung (2012)
Analysis of reliability indices from Humphrey visual field tests in an urban glaucoma population.
C. Birt (1997)
Bayesian t tests for accepting and rejecting the null hypothesis
J. Rouder (2009)
The Impact of Location of Progressive Visual Field Loss on Longitudinal Changes in Quality of Life of Patients with Glaucoma.
Ricardo Y Abe (2016)
Evaluation of retinal nerve fiber layer progression in glaucoma a prospective analysis with neuroretinal rim and visual field progression.
C. K. S. Leung (2011)
Efficacy of a Deep Learning System for Detecting Glaucomatous Optic Neuropathy Based on Color Fundus Photographs.
Z. Li (2018)
Independent component analysis, A new concept?
P. Comon (1994)
From Machine to Machine: An OCT-trained Deep Learning Algorithm for Objective Quantification of Glaucomatous Damage in Fundus Photographs
F. Medeiros (2019)
Assessment of the reliability of standard automated perimetry in regions of glaucomatous damage.
S. Gardiner (2014)
A Study of Cross-Validation and Bootstrap for Accuracy Estimation and Model Selection
R. Kohavi (1995)
Glaucomatous damage of the macula
D. Hood (2013)
Impact of visual field loss on health-related quality of life in glaucoma: the Los Angeles Latino Eye Study.
R. Mckean-Cowdin (2008)
The Relationship Between the Mean Deviation Slope and Follow-up Intraocular Pressure in Open-angle Glaucoma Patients
T. Fukuchi (2013)
Clinical Decisions In Glaucoma
E. Hodapp (1993)
Detection of Longitudinal Visual Field Progression in Glaucoma Using Machine Learning.
S. Yousefi (2018)
The effect of visual field defects on driving performance: a driving simulator study.
Tanja R. M. Coeckelbergh (2002)
Glaucoma Monitoring Using Manifold Learning and Unsupervised Clustering
S. Yousefi (2018)
Driving performance in patients with mild to moderate glaucomatous clinical vision changes.
J. Szlyk (2002)
Classification of visual field abnormalities in the ocular hypertension treatment study.
J. Keltner (2003)
Glaucoma and reading speed: the Salisbury Eye Evaluation project.
P. Ramulu (2009)
Detection of glaucomatous optic neuropathy with spectral-domain optical coherence tomography: a retrospective training and validation deep-learning analysis
A. Ran (2019)

This paper is referenced by
Semantic Scholar Logo Some data provided by SemanticScholar