The relationship between central visual field sensitivity and macular ganglion cell/inner plexiform layer thickness in glaucoma

Lee, Jiwoong; Morales, Esteban; Sharifipour, Farideh; Amini, Navid; Yu, Fei; Afifi, Abdelmonem A.; Coleman, Anne L.; Caprioli, Joseph; Nouri‐Mahdavi, Kouros

doi:10.1136/bjophthalmol-2016-309208

Cited by 50 publications

(53 citation statements)

References 26 publications

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“…Studies have explored structure-function relationships between various glaucoma outcome measures and threshold sensitivity in visual field testing. 23,24 We have evidence here that correlations of macular GCC thickness with VFs are comparable to those of peripapillary RNFL with VFs. Strictly GCC does not solely reflect loss of ganglion cells as the measure of GCC is a combination of RNFL, ganglion cell layer and inner plexiform layer.…”

Section: Discussionmentioning

confidence: 53%

Reduced Macular Vessel Density and Capillary Perfusion in Glaucoma Detected Using OCT Angiography

Sebastian

Chu

et al. 2019

Current Eye Research

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Aims: To evaluate retinal vasculature changes in primary open angle glaucoma (POAG) and whether the functional visual loss correlates with parameters obtained using optical coherence tomography angiography (OCTA). Materials and Methods: OCT and OCTA images were collected from 116 POAG eyes and 40 normal eyes in a prospective, cross-sectional observational study. Glaucomatous eyes were further divided into three groups according to a Glaucoma Staging System. Measurements of macular vessel density, ganglion cell complex (GCC), and disc retinal nerve fibre layer (RNFL) thickness were compared among groups. Results: The macular vessel density, GCC, and RNFL are significantly reduced in POAG compared to normal eyes that also corresponds to the severity of glaucoma (Kruskal-Wallis test with Dunnett's correction; p < 0.0001). Visual field mean deviation correlates significantly with macular vessel density (p = 0.0028, r = 0.3), GCC (p < 0.0001, r = 0.6), and RNFL (p = 0.008, r = 0.36) in POAG. There are significant correlations between GCC and RNFL (p < 0.0001, r = 0.76) as well as macular vessel density (p < 0.0001, r = 0.48). Increased age also correlates with reduced macular vessel density in both normal (p = 0.0002, r = 0.49) and glaucomatous eyes (p < 0.0001, r = 0.48), but a greater proportionate reduction of vessel density is seen in glaucomatous eyes. Conclusion: Reduced macular vessel density occurs in POAG despite of age-related changes, which also correlates with reductions in RNFL and GCC measurements. OCTA can detect microstructural defects and offers potential to facilitate diagnosis of glaucoma.

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Section: Discussionmentioning

confidence: 53%

Reduced Macular Vessel Density and Capillary Perfusion in Glaucoma Detected Using OCT Angiography

Sebastian

Chu

et al. 2019

Current Eye Research

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“…In that study, macular GCIPL thickness reflected macular functional damage well in more advanced glaucoma, the stage wherein structural change is thought to be less sensitive and informative for indicating disease status. Recently, a pattern of correlation between GCIPL thickness and 10-2 SAP test points, presented by Lee et al , showed localised arcuate characteristics in the central macula, which were well matched with the patterns of GCIPL defect themselves 39. All of the reports above noted have provided strong evidence that VF function can be relatively well inferred from macular parameters, especially in the central macular region.…”

Section: Introductionmentioning

confidence: 67%

Macular imaging by optical coherence tomography in the diagnosis and management of glaucoma

Kim

Park

2017

Br J Ophthalmol

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The macular area is important to the detection of glaucomatous retinal ganglion cell (RGC) damage. Macular thickness complementary to peripapillary retinal nerve fibre layer (RNFL) thickness can well reflect glaucomatous damage, given that the macula contains more than 50% of the RGCs in a multilayered pattern and larger RGC bodies compared with their axons. Thus, macular ganglion cell thickness parameters recently have been considered to be an effective glaucoma-diagnostic tool comparable to RNFL thickness parameters. Furthermore, spectral-domain optical coherence tomography ganglion cell-inner plexiform layer thickness and deviation maps can provide additional information essential for distinguishing glaucomatous changes from other, myopia-associated or macular disease-associated changes. Therefore, our aim with this study was to review the clinical application of macular imaging by optical coherence tomography and to provide essential clinical tips for its use in the diagnosis and management of glaucoma.

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“…Other studies relied on different types of OCT inputs to get predictions of SAP. 7,[33][34][35][36][37] Christopher et al 37 achieved best performance when using en face images to predict SAP global metrics and sectoral averages. It should be noted, however, that our main purpose was to use the CNN to develop an SF map.…”

Section: Discussionmentioning

confidence: 99%

Artificial Intelligence Mapping of Structure to Function in Glaucoma

Mariottoni

Datta

Dov

et al. 2020

Trans. Vis. Sci. Tech.

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Purpose:To develop an artificial intelligence (AI)-based structure-function (SF) map relating retinal nerve fiber layer (RNFL) damage on spectral domain optical coherence tomography (SDOCT) to functional loss on standard automated perimetry (SAP). Methods:The study included 26,499 pairs of SAP and SDOCT from 15,173 eyes of 8878 patients with glaucoma or suspected of having the disease extracted from the Duke Glaucoma Registry. The data set was randomly divided at the patient level in training and test sets. A convolutional neural network (CNN) was initially trained and validated to predict the 52 sensitivity threshold points of the 24-2 SAP from the 768 RNFL thickness points of the SDOCT peripapillary scan. Simulated localized RNFL defects of varied locations and depths were created by modifying the normal average peripapillary RNFL profile. The simulated profiles were then fed to the previously trained CNN, and the topographic SF relationships between structural defects and SAP functional losses were investigated. Results:The CNN predictions had an average correlation coefficient of 0.60 (P < 0.001) with the measured values from SAP and a mean absolute error of 4.25 dB. Simulated RNFL defects led to well-defined arcuate or paracentral visual field losses in the opposite hemifield, which varied according to the location and depth of the simulations. Conclusions:A CNN was capable of predicting SAP sensitivity thresholds from SDOCT RNFL thickness measurements and generate an SF map from simulated defects.Translational Relevance: AI-based SF map improves the understanding of how SDOCT losses translate into detectable SAP damage.

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The relationship between central visual field sensitivity and macular ganglion cell/inner plexiform layer thickness in glaucoma

Abstract: NCT01742819.

Cited by 50 publications

References 26 publications

Reduced Macular Vessel Density and Capillary Perfusion in Glaucoma Detected Using OCT Angiography

Reduced Macular Vessel Density and Capillary Perfusion in Glaucoma Detected Using OCT Angiography

Macular imaging by optical coherence tomography in the diagnosis and management of glaucoma

Artificial Intelligence Mapping of Structure to Function in Glaucoma

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