Prediction of glaucoma severity using parameters from the electroretinogram

Sarossy, Marc; Crowston, Jonathan G; Kumar, Dinesh; Weymouth, Anne; Wu, Zhichao

doi:10.1038/s41598-021-03421-6

Cited by 13 publications

(14 citation statements)

References 37 publications

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“…The findings of this study build upon our previous work, further reinforcing how there is substantially more information present in the photopic ERG beyond the PhNR amplitude or ratio of PhNR to b-wave alone, 21 which currently remain the primary recommended measures for assessing RGC function on the ERG. 11 However, the improved prediction of glaucoma severity at cross-section when incorporating features from time–frequency analyses does not guarantee its improved utility for the clinically important task of predicting RGC loss in glaucoma, which remains a significant challenge.…”

Section: Discussionsupporting

confidence: 77%

“…We have previously shown that a combination of key amplitude markers of the ERG—namely, the a-wave, b-wave, i-wave, and PhNR (PhNR1 and PhNR2) amplitudes—better predicts glaucoma severity than the PhNR amplitude alone. 21 These findings underscore how a photopic ERG contains more information about RGC function than is captured by conventional PhNR measures alone.…”

Section: Introductionmentioning

confidence: 74%

“…For each ensemble average of ERG traces, amplitude markers were determined as described previously. 21 Briefly, the a-wave trough was determined as the first negative trough after the stimulus onset, and the b-wave peak was found as the first peak after the a-wave. The i-wave was found as the first peak or inflexion point after the b-wave.…”

Section: Methodsmentioning

confidence: 99%

“…To examine the value of time–frequency features from the ERG for predicting glaucoma severity based on the eRGC counts, predictive models utilizing the amplitude markers (a-wave, b-wave, i-wave, PhNR1, and PhNR2) were used as a base-case scenario for comparisons, given that we previously demonstrated that the use of this set of amplitude markers improved the prediction of glaucoma severity compared with the PhNR measure alone. 21 The performance of the base-case amplitude marker models was compared with that of models using these features plus additional DWT and/or MP features.…”

Section: Methodsmentioning

confidence: 99%

“…The models tested were a simple linear model and a multivariate adaptive regression spline (MARS) model, 52 an interpretable type of machine learning model that, as we have previously shown, improves the prediction of glaucoma severity with the ERG. 21 The outcome measure of a MARS model is a linear sum of piecewise linear functions. The Caret 53 package (version 6.0-86) in R was used for model fitting and tuning.…”

Section: Methodsmentioning

confidence: 99%

See 4 more Smart Citations

Time–Frequency Analysis of ERG With Discrete Wavelet Transform and Matching Pursuits for Glaucoma

Sarossy

Crowston

Kumar

et al. 2022

Trans. Vis. Sci. Tech.

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Purpose To examine the performance of two time–frequency feature extraction techniques applied to electroretinograms (ERGs) for the prediction of glaucoma severity. Methods ERGs targeting the photopic negative response were obtained in 103 eyes of 55 patients with glaucoma. Features from the ERG recordings were extracted using two time–frequency extraction techniques based on the discrete wavelet transform (DWT) and the matching pursuit (MP) decomposition. Amplitude markers of the time-domain signal were also extracted. Linear and multivariate adaptive regression spline (MARS) models were fitted using combinations of these features to predict estimated retinal ganglion cell counts, a measure of glaucoma disease severity derived from standard automated perimetry and optical coherence tomography imaging. Results Predictive models using features from the time–frequency analyses—using both DWT and MP—combined with amplitude markers outperformed predictive models using the markers alone with linear ( P = 0.001) and MARS ( P ≤ 0.011) models. For example, the proportions of variance ( R 2 ) explained by the MARS model using the DWT and MP features with amplitude markers were 0.53 and 0.63, respectively, compared to 0.34 for the model using the markers alone ( P = 0.011 and P = 0.001, respectively). Conclusions Novel time–frequency features extracted from the photopic ERG substantially added to the prediction of glaucoma severity compared to using the time-domain amplitude markers alone. Translational Relevance Substantial information about retinal ganglion cell dysfunction exists in the time–frequency domain of ERGs that could be useful in the management of glaucoma.

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Section: Discussionsupporting

confidence: 77%

Section: Introductionmentioning

confidence: 74%

Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

See 3 more Smart Citations

Time–Frequency Analysis of ERG With Discrete Wavelet Transform and Matching Pursuits for Glaucoma

Sarossy

Crowston

Kumar

et al. 2022

Trans. Vis. Sci. Tech.

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Three-Dimensional Structural Phenotype of the Optic Nerve Head as a Function of Glaucoma Severity

Braeu,

Chuangsuwanich,

Tun

et al. 2023

JAMA Ophthalmol

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ImportanceThe 3-dimensional (3-D) structural phenotype of glaucoma as a function of severity was thoroughly described and analyzed, enhancing understanding of its intricate pathology beyond current clinical knowledge.ObjectiveTo describe the 3-D structural differences in both connective and neural tissues of the optic nerve head (ONH) between different glaucoma stages using traditional and artificial intelligence–driven approaches.Design, Setting, and ParticipantsThis cross-sectional, clinic-based study recruited 541 Chinese individuals receiving standard clinical care at Singapore National Eye Centre, Singapore, and 112 White participants of a prospective observational study at Vilnius University Hospital Santaros Klinikos, Vilnius, Lithuania. The study was conducted from May 2022 to January 2023. All participants had their ONH imaged using spectral-domain optical coherence tomography and had their visual field assessed by standard automated perimetry.Main Outcomes and Measures(1) Clinician-defined 3-D structural parameters of the ONH and (2) 3-D structural landmarks identified by geometric deep learning that differentiated ONHs among 4 groups: no glaucoma, mild glaucoma (mean deviation [MD], ≥−6.00 dB), moderate glaucoma (MD, −6.01 to −12.00 dB), and advanced glaucoma (MD, &lt;−12.00 dB).ResultsStudy participants included 213 individuals without glaucoma (mean age, 63.4 years; 95% CI, 62.5-64.3 years; 126 females [59.2%]; 213 Chinese [100%] and 0 White individuals), 204 with mild glaucoma (mean age, 66.9 years; 95% CI, 66.0-67.8 years; 91 females [44.6%]; 178 Chinese [87.3%] and 26 White [12.7%] individuals), 118 with moderate glaucoma (mean age, 68.1 years; 95% CI, 66.8-69.4 years; 49 females [41.5%]; 97 Chinese [82.2%] and 21 White [17.8%] individuals), and 118 with advanced glaucoma (mean age, 68.5 years; 95% CI, 67.1-69.9 years; 43 females [36.4%]; 53 Chinese [44.9%] and 65 White [55.1%] individuals). The majority of ONH structural differences occurred in the early glaucoma stage, followed by a plateau effect in the later stages. Using a deep neural network, 3-D ONH structural differences were found to be present in both neural and connective tissues. Specifically, a mean of 57.4% (95% CI, 54.9%-59.9%, for no to mild glaucoma), 38.7% (95% CI, 36.9%-40.5%, for mild to moderate glaucoma), and 53.1 (95% CI, 50.8%-55.4%, for moderate to advanced glaucoma) of ONH landmarks that showed major structural differences were located in neural tissues with the remaining located in connective tissues.Conclusions and RelevanceThis study uncovered complex 3-D structural differences of the ONH in both neural and connective tissues as a function of glaucoma severity. Future longitudinal studies should seek to establish a connection between specific 3-D ONH structural changes and fast visual field deterioration and aim to improve the early detection of patients with rapid visual field loss in routine clinical care.

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Gene Expression and Editing in Mammalian Retinal Ganglion Cells

Tran,

Xu,

et al. 2024

Essentials in Ophthalmology

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Prediction of glaucoma severity using parameters from the electroretinogram

Cited by 13 publications

References 37 publications

Time–Frequency Analysis of ERG With Discrete Wavelet Transform and Matching Pursuits for Glaucoma

Time–Frequency Analysis of ERG With Discrete Wavelet Transform and Matching Pursuits for Glaucoma

Three-Dimensional Structural Phenotype of the Optic Nerve Head as a Function of Glaucoma Severity

Gene Expression and Editing in Mammalian Retinal Ganglion Cells

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