The Relationship Between Bruch's Membrane Opening-Minimum Rim Width and Retinal Nerve Fiber Layer Thickness and a New Index Using a Neural Network

Park, Keunheung; Kim, Jinmi; Lee, Jiwoong

doi:10.1167/tvst.7.4.14

Cited by 28 publications

(25 citation statements)

References 45 publications

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“…Combining two or more parameters may also further improve the discriminatory ability of the algorithm. 40 In summary, we used the BMO-MRW from SDOCT to train a deep learning algorithm to predict quantitative data about the degree of neuroretinal damage when assessing an optic disc photograph. The predicted quantitative data was correlated with the BMO-MRW value from SDOCT and showed high diagnostic discrimination for glaucomatous visual field loss.…”

Section: Discussionmentioning

confidence: 99%

A Deep Learning Algorithm to Quantify Neuroretinal Rim Loss From Optic Disc Photographs

Thompson

Jammal

Medeiros

2019

American Journal of Ophthalmology

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Purpose: To train a deep learning (DL) algorithm that quantifies glaucomatous neuroretinal damage on fundus photographs using the minimum rim width relative to Bruch's membrane opening (BMO-MRW) from spectral domain-optical coherence tomography (SDOCT). Design: Cross-sectional study Methods: 9,282 pairs of optic disc photographs and SDOCT optic nerve head scans from 927 eyes of 490 subjects were randomly divided into the validation plus training (80%) and test sets (20%). A DL convolutional neural network was trained to predict the SDOCT BMO-MRW global and sector values when evaluating optic disc photographs. The predictions of the DL network were compared to the actual SDOCT measurements. The area under the receiver operating curve (AUC) was used to evaluate the ability of the network to discriminate glaucomatous visual field loss from normal eyes. Results: The DL predictions of global BMO-MRW from all optic disc photos in the test set (mean ± standard deviation [SD]: 228.8±63.1μm) were highly correlated with the observed values from SDOCT (mean ± SD: 226.0±73.8μm) (Pearson's r=0.88; R 2 =77%; P<0.001), with mean absolute error of the predictions of 27.8μm. The AUCs for discriminating glaucomatous from healthy eyes with the DL predictions and actual SDOCT global BMO-MRW measurements were 0.945 (95% CI:0.874-0.980) and 0.933 (95% CI:0.856-0.975), respectively (P=0.587). Conclusions: A DL network can be trained to quantify the amount of neuroretinal damage on optic disc photographs using SDOCT BMO-MRW as a reference. This algorithm showed high accuracy for glaucoma detection, and may potentially eliminate the need for human gradings of disc photos.

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Section: Discussionmentioning

confidence: 99%

A Deep Learning Algorithm to Quantify Neuroretinal Rim Loss From Optic Disc Photographs

Thompson

Jammal

Medeiros

2019

American Journal of Ophthalmology

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“…27 In patients without ON history, probably only RNFL and retinal ganglion cell loss due to retrograde axonal degeneration are observed. 3,27 Park et al 16 studied S-RNFL and BMO-MRW techniques in glaucoma. They found that the correlation was most robust in T sectors of advanced glaucoma patients but weak to moderate correlation values in HC subjects.…”

Section: Discussionmentioning

confidence: 99%

“…This situation enables precise examination of the relevant structures and ensures accurate comparisons with reference data, allowing for a highly sensitive assessment of structural change. 12,15,16 In this study, we aimed to investigate the tissue-function (visual functions and disability in MS) relationship in patients with MS by using new parameters of OCT, which are BMO-MRW and RNFL measured using the APS™.…”

Section: Introductionmentioning

confidence: 99%

Bruch’s membrane opening-minimum rim width: An alternative OCT biomarker study for multiple sclerosis

Özbilen

Gündüz

Kartal

et al. 2021

European Journal of Ophthalmology

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Purpose: Bruch’s membrane opening-minimum rim width (BMO-MRW) and RNFL measured using anatomic positioning system (APS-RNFL) are novel OCT methods and remained unexplored in MS patients. To investigate the novel parameters of spectral-domain OCT as an alternative biomarker in patients with multiple sclerosis (MS). Methods: Retrospective cohort study; participants consisted of relapsing-remitting MS (RRMS) patients and healthy controls (HC). Eyes were classified according to the presence of MS and previous optic neuritis (ON). Measurements of standard peripapillary RNFL (S-RNFL), BMO-MRW, and APS-RNFL were performed. Result: A total of 244 eyes of 122 participants (MS-patients: 63, HC: 59) were included in the study. Fifty-one eyes had a history of previous ON. In almost all measured parameters, neuroretinal rim thicknesses were observed the thinnest in eyes with ON history between all subgroups. S-RNFL and APS-RNFL techniques showed the difference in neuroretinal rim thickness in all three subjects (ON+, ON−, and HC). However, BMO-MRW, on the other hand, could not distinguish between ON(−) patients and HC. The relationship between OCT parameters and EDSS were observed only in eyes with an ON history in all three techniques. A meaningful model with 78% accuracy was obtained by using only the OCT parameters as risk factors. In the ROC analysis, no parameters were found to have acceptable high sensitivity and specificity. BMO-MRW was statistically weaker in every aspect than other RNFL techniques. Conclusion: The novel APS-RNFL technique appears to be a bit more reliable alternative to S-RNFL technique to support therapeutic decision-making in MS. BMO-MRW has not been found as a successful alternative to S-RNFL.

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“…A recent previous study showed that neuroretinal rim thinning of BMO-MRW may precede RNFL thinning through a nonlinear piecewise regression analysis, echoing the findings in several previous clinical and experimental glaucoma model studies. 47 – 49 Hence, optic nerve parameters may be earlier indicators of glaucomatous damage compared with RNFL thickness.…”

Section: Discussionmentioning

confidence: 99%

Three-dimensional Neuroretinal Rim Thickness and Visual Fields in Glaucoma: A Broken-stick Model

Liu

McClurkin

Tsikata

et al. 2020

Journal of Glaucoma

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I. Analysis of the structure-function relationship in the linear scale with the broken-stick model We also applied the broken-stick model to the VF data and the MDB structural parameters both in the linear scale. When the VF TD data was transformed to the linear scale, the observation that "the structurefunction relationship displayed a plateau, with neuroretinal rim thickness values being unrelated to VF values" was not readily visualized in some of quadrants/ sectors (Figure S1). Further statistical evaluation shows that the slopes above the tipping point are not significantly different from 0, as shown in Table S1. We added in a model-agnostic lowess smoothing curve to compare with the fitted broken-stick model on the data of unlogged VF TD against the MDB. The broken stick model fit didn't match with the lowess fit for the Temporal-Inferior region and the Inferior quadrant. Furthermore, the breakpoint didn't exist for the nasal and temporal quadrants and the TS and NI octants, as shown in the table below where the pvalues are greater than 0.05 (Table S1). For the breakpoints which did exist (i.e. the global value, superior and inferior quadrants, TI and NS sectors), the fitted breakpoints in the linear scale were different for the breakpoints obtained in the logarithmic fits and not encompassed by their respective confidence intervals from the logarithmic scale. As a final remark, indeed the question of whether the structure-function relationship between the VF data and the neuroretinal rim data should be established as a logarithmic-linear scale or as a linear-linear scale has been actively studied in the past two decades. 1-4 A variety of models have been employed to describe the structure-function relationship in both the linear and curvilinear regimes. With this rich history of research on this topic behind us, we primarily presented the data by looking at the VF in the logarithmic scale (or in its native "untransformed" form) since it is more clinically utilized. Also, the logarithmic scale helps to distinguish among the moderate and severe glaucomatous patients.

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The Relationship Between Bruch's Membrane Opening-Minimum Rim Width and Retinal Nerve Fiber Layer Thickness and a New Index Using a Neural Network

Cited by 28 publications

References 45 publications

A Deep Learning Algorithm to Quantify Neuroretinal Rim Loss From Optic Disc Photographs

A Deep Learning Algorithm to Quantify Neuroretinal Rim Loss From Optic Disc Photographs

Bruch’s membrane opening-minimum rim width: An alternative OCT biomarker study for multiple sclerosis

Three-dimensional Neuroretinal Rim Thickness and Visual Fields in Glaucoma: A Broken-stick Model

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