Baseline structural characteristics of the optic nerve head and retinal nerve fiber layer are associated with progressive visual field loss in patients with open-angle glaucoma

Siesky, Brent; Wentz, Scott; Janulevičienė, Ingrida; Kim, Daniel H; Burgett, Kendall M.; Vercellin, Alice Chandra Verticchio; Rowe, Lucas W.; Eckert, George J.; Harris, Alon

doi:10.1371/journal.pone.0236819

Cited by 11 publications

(9 citation statements)

References 24 publications

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“…Despite its normal mean value of intraocular pressure (IOP) as is expected of healthy eyes, the signature cluster has the highest mean values of average CDR and cup volume of all metaclusters across all 3 age groups ( Table 2). In a recently published longitudinal study that started from baseline values and was run over a 5-year period, the covariates which had statistically significant increase in glaucomatous progression included CDR and cup volume 31 . We understand that it would perhaps be ideal to follow-up healthy individuals and measure the changes in their clinical covariates as they age in order to classify the ONH phenotypes via supervised learning.…”

Section: Discussionmentioning

confidence: 99%

Circular functional analysis of OCT data for precise identification of structural phenotypes in the eye

Ali

Wainwright

Petersen

et al. 2021

Preprint

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Progressive optic neuropathies such as glaucoma are major causes of blindness globally. Multiple sources of subjectivity and analytical challenges are often encountered by the clinicians in the process of early diagnosis and clinical management of these diseases. In glaucoma, the structural damage is often characterized by neuroretinal rim (NRR) thinning of the optic nerve head, and other clinical parameters. Optical coherence tomography (OCT) is a popular and quantitative eye imaging platform for precise and reproducible measurement of such parameters in the clinic. Baseline structural heterogeneity in the eyes can play a key role in the progression of optic neuropathies, and thus present challenges to clinical decision-making. To address this, large and diverse normative OCT databases with mathematically precise description of phenotypes can help with early detection and characterization of the different phenotypes that are encountered in the clinic. In this study, we generated a new large dataset of OCT generated high-resolution circular data on NRR phenotypes, along with other clinical covariates, of nearly 4,000 healthy eyes as part of a well-established clinical cohort (LVPEI-GLEAMS) of Asian Indian participants. In this study, we (1) generated high-resolution circular OCT measurements of NRR thickness in a given eye, (2) introduced CIFU, a new computational pipeline for CIrcular FUnctional data modeling and analysis that is demonstrated using the OCT dataset, and (3) addressed the disparity of representation of the Asian Indian population in normative OCT databases. We demonstrated CIFU by unsupervised circular functional clustering of the OCT NRR data, meta-clustering to characterize the clustering output using clinical covariates, and presenting a circular visualization of the results. Upon stratification by age, we identified a healthy NRR phenotype cluster in the age group 40-49 years with predictive potential for glaucoma.

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Section: Discussionmentioning

confidence: 99%

Circular functional analysis of OCT data for precise identification of structural phenotypes in the eye

Ali

Wainwright

Petersen

et al. 2021

Preprint

View full text Add to dashboard Cite

show abstract

“…Despite its normal mean value of intraocular pressure (IOP) as is expected of healthy eyes, the signature cluster has the highest mean values of average CDR and cup volume of all metaclusters across all 3 age groups (Table 2 ). In a recently published longitudinal study that started from baseline values and was run over a 5-year period, the covariates which had statistically significant increase in glaucomatous progression included CDR and cup volume 43 . We understand that it would perhaps be ideal to follow-up healthy individuals and measure the changes in their clinical covariates as they age in order to classify the ONH phenotypes via supervised learning.…”

Section: Discussionmentioning

confidence: 99%

Circular functional analysis of OCT data for precise identification of structural phenotypes in the eye

Ali

Wainwright

Petersen

et al. 2021

Sci Rep

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Progressive optic neuropathies such as glaucoma are major causes of blindness globally. Multiple sources of subjectivity and analytical challenges are often encountered by clinicians in the process of early diagnosis and clinical management of these diseases. In glaucoma, the structural damage is often characterized by neuroretinal rim (NRR) thinning of the optic nerve head, and other clinical parameters. Baseline structural heterogeneity in the eyes can play a key role in the progression of optic neuropathies, and present challenges to clinical decision-making. We generated a dataset of Optical Coherence Tomography (OCT) based high-resolution circular measurements on NRR phenotypes, along with other clinical covariates, of 3973 healthy eyes as part of an established clinical cohort of Asian Indian participants. We introduced CIFU, a new computational pipeline for CIrcular FUnctional data modeling and analysis. We demonstrated CIFU by unsupervised circular functional clustering of the OCT NRR data, followed by meta-clustering to characterize the clusters using clinical covariates, and presented a circular visualization of the results. Upon stratification by age, we identified a healthy NRR phenotype cluster in the age group 40–49 years with predictive potential for glaucoma. Our dataset also addresses the disparity of representation of this particular population in normative OCT databases.

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“…Siesky et al [23] inspected progression of open angle glaucoma within 5 years of time. A total of 112 open-angle glaucoma patients were examined after every six months for the period of 5 years.…”

Section: Optical Coherence Tomography (Oct) Image Analysismentioning

confidence: 99%

A Comprehensive Convolutional Neural Network Survey to Detect Glaucoma Disease

Zafar

Aamir

Nawi

et al. 2022

Mobile Information Systems

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Due to recent advances in computer technology and the accessibility of large datasets, deep learning has become at the forefront of artificial intelligence and on various tasks, especially those related to image classification and modelling, its performance is often equal to or even better than human appreciation. Ophthalmology has always been in an ideal position to employ one of CNN’s most popular deep learning algorithms to evaluate vast volumes of data from these tests since it is one of the health practices that focuses extensively on imaging. Glaucoma is one among the situations in which CNN can benefit from the enormous quantity of data collected by tests that assess the anatomy as well as function of the optic nerve and macula. We discussed the recommended use of CNN algorithm for specific glaucoma scenarios, such as fundus photography screening and diagnosis and detection of the course of glaucoma through OCT imaging modality. The purpose of this article is not only to critically examine and discuss the latest applications of CNN models in glaucoma but also to focus on the challenges associated with developing such models for screening, diagnosis, and progress detection. After a brief overview of the clinical practices and their comparison with conventional clinical methods, we discussed training and validation of CNN algorithm and how it was developed and why it is particularly suitable for glaucoma. The following features make our contribution worthwhile and unique among the reviews of similar kind: (i) our review classifies the existing literature with respect to detection of the glaucoma disease using conventional and nonconventional approaches; (ii) it covers a very different outlook of the glaucoma disease by providing in-depth discussions of the existing works at different granularity levels, that is, from primary to mediatory to the severe level; (iii) this state-of-the-art review covers each article in the following dimensions: the computer-based approach to tomographic model; analysis of different datasets; and summarizing the literature review in a disciplined way by mentioning the research gap concluded with discussion on future work.

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Baseline structural characteristics of the optic nerve head and retinal nerve fiber layer are associated with progressive visual field loss in patients with open-angle glaucoma

Abstract: To examine the relationship between baseline structural characteristics of the optic nerve head (ONH) and retinal nerve fiber layer (RNFL) and functional disease progression in patients with open-angle glaucoma (OAG) over 5 years.

Cited by 11 publications

References 24 publications

Circular functional analysis of OCT data for precise identification of structural phenotypes in the eye

Circular functional analysis of OCT data for precise identification of structural phenotypes in the eye

Circular functional analysis of OCT data for precise identification of structural phenotypes in the eye

A Comprehensive Convolutional Neural Network Survey to Detect Glaucoma Disease

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