Diagnostic Ability of Individual Macular Layers by Spectral-Domain OCT in Different Stages of Glaucoma

Chua, Jacqueline; Tan, Bingyao; Ke, Mengyuan; Schwarzhans, Florian; Vass, Clemens; Wong, Damon Wing Kee; Nongpiur, Monisha E.; Chua, Matthew Chin Heng; Yao, Xinwen; Cheng, Ching‐Yu; Aung, Tin; Schmetterer, Leopold

doi:10.1016/j.ogla.2020.04.003

Cited by 27 publications

(21 citation statements)

References 43 publications

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“…One trained grader masked to the participant’s characteristics reviewed the quality of OCT datasets. Eyes with poor quality images (signal strength <6 and/or movement artifacts causing off-centration, breakages, inconsistent signal intensity across the scan (poorly or unevenly illuminated scans), algorithm failures resulting in segmentation errors, and/or eyes with ocular pathology and missing variables were excluded ( Chua et al, 2020b ).…”

Section: Methodsmentioning

confidence: 99%

A multi-regression approach to improve optical coherence tomography diagnostic accuracy in multiple sclerosis patients without previous optic neuritis

Chua

Bostan

et al. 2022

NeuroImage: Clinical

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Section: Methodsmentioning

confidence: 99%

A multi-regression approach to improve optical coherence tomography diagnostic accuracy in multiple sclerosis patients without previous optic neuritis

Chua

Bostan

et al. 2022

NeuroImage: Clinical

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“…This retrospective cross-sectional study was conducted from January 2012 to March 2021 and included Chinese participants with glaucoma from 4 clinical studies (2009-2021) 32,33 and controls from the Singapore Epidemiology of Eye Disease program, 34 a population-based study (2009)(2010)(2011)(2012)(2013)(2014)(2015)(2016)(2017)(2018)(2019)(2020)(2021) performed at the Singapore Eye Research Institute in Singapore. Additional data from a clinical study of non-Hispanic White participants from the Carol Davila University of Medicine and Pharmacy, Bucharest, Romania (2020-2021), were also included.…”

Section: Participantsmentioning

confidence: 99%

Evaluation of Generative Adversarial Networks for High-Resolution Synthetic Image Generation of Circumpapillary Optical Coherence Tomography Images for Glaucoma

et al. 2022

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IMPORTANCE Deep learning (DL) networks require large data sets for training, which can be challenging to collect clinically. Generative models could be used to generate large numbers of synthetic optical coherence tomography (OCT) images to train such DL networks for glaucoma detection.OBJECTIVE To assess whether generative models can synthesize circumpapillary optic nerve head OCT images of normal and glaucomatous eyes and determine the usability of synthetic images for training DL models for glaucoma detection.DESIGN, SETTING, AND PARTICIPANTS Progressively growing generative adversarial network models were trained to generate circumpapillary OCT scans. Image gradeability and authenticity were evaluated on a clinical set of 100 real and 100 synthetic images by 2 clinical experts. DL networks for glaucoma detection were trained with real or synthetic images and evaluated on independent internal and external test data sets of 140 and 300 real images, respectively.MAIN OUTCOMES AND MEASURES Evaluations of the clinical set between the experts were compared. Glaucoma detection performance of the DL networks was assessed using area under the curve (AUC) analysis. Class activation maps provided visualizations of the regions contributing to the respective classifications.RESULTS A total of 990 normal and 862 glaucomatous eyes were analyzed. Evaluations of the clinical set were similar for gradeability (expert 1: 92.0%; expert 2: 93.0%) and authenticity (expert 1: 51.8%; expert 2: 51.3%). The best-performing DL network trained on synthetic images had AUC scores of 0.97 (95% CI, 0.95-0.99) on the internal test data set and 0.90 (95% CI, 0.87-0.93) on the external test data set, compared with AUCs of 0.96 (95% CI, 0.94-0.99) on the internal test data set and 0.84 (95% CI, 0.80-0.87) on the external test data set for the network trained with real images. An increase in the AUC for the synthetic DL network was observed with the use of larger synthetic data set sizes. Class activation maps showed that the regions of the synthetic images contributing to glaucoma detection were generally similar to that of real images.CONCLUSIONS AND RELEVANCE DL networks trained with synthetic OCT images for glaucoma detection were comparable with networks trained with real images. These results suggest potential use of generative models in the training of DL networks and as a means of data sharing across institutions without patient information confidentiality issues.

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“…Ustaoglu et al tried to determine the discriminating performance of the macular GC-IPL parameters between all the consecutive stages of glaucoma and to compare it with the discriminating performances of the RNFL and ONH parameters [ 53 ]. Chua et al compared the diagnostic ability of macular intraretinal layer thickness with cRNFL thickness for detection of early, moderate, and advanced glaucoma [ 13 ]. Similarly, here, we tried to use machine learning methods to build reliable classifiers and study the importance of different OCT parameters in glaucoma of varying severities.…”

Section: Discussionmentioning

confidence: 99%

“…Although the abundant OCT parameters can give the clinicians a comprehensive understanding of the ocular structural changes, they may also cause confusion especially when the classification results conflict, usually seen in early-stage glaucoma due to the subtle structural changes [ 6 ]. There have been many studies investigating the diagnostic accuracy of parameters between normal and glaucomatous eyes [ 7 , 8 , 9 , 10 , 11 , 12 , 13 ], but most of them focused on individual parameters. However, in actual clinical situations, clinicians usually make the final diagnosis after considering all the parameters.…”

Section: Introductionmentioning

confidence: 99%

Comparison of Different Machine Learning Classifiers for Glaucoma Diagnosis Based on Spectralis OCT

Shen

et al. 2021

Diagnostics

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Early detection is important in glaucoma management. By using optical coherence tomography (OCT), the subtle structural changes caused by glaucoma can be detected. Though OCT provided abundant parameters for comprehensive information, clinicians may be confused once the results conflict. Machine learning classifiers (MLCs) are good tools for considering numerous parameters and generating reliable diagnoses in glaucoma practice. Here we aim to compare different MLCs based on Spectralis OCT parameters, including circumpapillary retinal nerve fiber layer (cRNFL) thickness, Bruch’s membrane opening-minimum rim width (BMO-MRW), Early Treatment Diabetes Retinopathy Study (ETDRS) macular thickness, and posterior pole asymmetry analysis (PPAA), in discriminating normal from glaucomatous eyes. Five MLCs were proposed, namely conditional inference trees (CIT), logistic model tree (LMT), C5.0 decision tree, random forest (RF), and extreme gradient boosting (XGBoost). Logistic regression (LGR) was used as a benchmark for comparison. RF was shown to be the best model. Ganglion cell layer measurements were the most important predictors in early glaucoma detection and cRNFL measurements were more important as the glaucoma severity increased. The global, temporal, inferior, superotemporal, and inferotemporal sites were relatively influential locations among all parameters. Clinicians should cautiously integrate the Spectralis OCT results into the entire clinical picture when diagnosing glaucoma.

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Diagnostic Ability of Individual Macular Layers by Spectral-Domain OCT in Different Stages of Glaucoma

Cited by 27 publications

References 43 publications

A multi-regression approach to improve optical coherence tomography diagnostic accuracy in multiple sclerosis patients without previous optic neuritis

A multi-regression approach to improve optical coherence tomography diagnostic accuracy in multiple sclerosis patients without previous optic neuritis

Evaluation of Generative Adversarial Networks for High-Resolution Synthetic Image Generation of Circumpapillary Optical Coherence Tomography Images for Glaucoma

Comparison of Different Machine Learning Classifiers for Glaucoma Diagnosis Based on Spectralis OCT

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