Correlation between nerve fibre layer thickness measured with spectral domain OCT and visual field in patients with different stages of glaucoma

Cvenkel, Barbara; Kontestabile, A Sket

doi:10.1007/s00417-010-1538-z

Cited by 42 publications

(26 citation statements)

References 23 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Glaucoma is a progressive optic neuropathy resulting in retinal ganglion cell loss and corresponding visual field defects [1,2]. Besides primary open angle glaucoma (POAG), subtypes like pseudoexfoliation glaucoma (PEX), and pigmentary glaucoma (PG) seem to be associated with greater intraocular pressure (IOP) peaks and fluctuations and more aggressive progressive glaucoma damage [3,4].…”

Section: Introductionmentioning

confidence: 99%

iStent inject in phakic open angle glaucoma

Klamann

Gonnermann

Pahlitzsch

et al. 2015

Graefes Arch Clin Exp Ophthalmol

View full text Add to dashboard Cite

show abstract

Section: Introductionmentioning

confidence: 99%

iStent inject in phakic open angle glaucoma

Klamann

Gonnermann

Pahlitzsch

et al. 2015

Graefes Arch Clin Exp Ophthalmol

View full text Add to dashboard Cite

show abstract

“…15 The current literature concludes that SD-OCT is an effective tool for detecting structural glaucomatous damage in glaucoma suspects, and in patients with early glaucoma and preperimetric glaucoma. 1,5,6,14,16 Cvenkel et al 17 reported thinner peripapillary RNFL (inner ring) in 35% of eyes with preperimetric glaucoma and 46% of the eyes with early glaucoma when compared with those of the healthy controls. Moreover, Wang et al 8 and Klaman et al 18 suggested that OCT could detect glaucomatous optic neuropathy prior to functional deterioration in SAP based on ONH and RNFL parameters.…”

Section: Discussionmentioning

confidence: 97%

Diagnostic Consistency and Relation Between Optical Coherence Tomography and Standard Automated Perimetry in Primary Open-Angle Glaucoma

Toprak

Yaylalı

Yıldırım

2015

Seminars in Ophthalmology

View full text Add to dashboard Cite

show abstract

“…Glaucomatous structural change is often apparent in patients with glaucoma without VF defects (preperimetric glaucoma) 22 23. Therefore, it may be possible to predict the presence of the VF deterioration from structural measurements, as it has been reported that there is a significant difference in structural measurements between patients with perimetric and preperimetric glaucoma 22.…”

Section: Introductionmentioning

confidence: 99%

Cross-sectional study: Does combining optical coherence tomography measurements using the ‘Random Forest’ decision tree classifier improve the prediction of the presence of perimetric deterioration in glaucoma suspects?

et al. 2013

View full text Add to dashboard Cite

ObjectivesTo develop a classifier to predict the presence of visual field (VF) deterioration in glaucoma suspects based on optical coherence tomography (OCT) measurements using the machine learning method known as the ‘Random Forest’ algorithm.DesignCase–control study.Participants293 eyes of 179 participants with open angle glaucoma (OAG) or suspected OAG.InterventionsSpectral domain OCT (Topcon 3D OCT-2000) and perimetry (Humphrey Field Analyser, 24-2 or 30-2 SITA standard) measurements were conducted in all of the participants. VF damage (Ocular Hypertension Treatment Study criteria (2002)) was used as a ‘gold-standard’ to classify glaucomatous eyes. The ‘Random Forest’ method was then used to analyse the relationship between the presence/absence of glaucomatous VF damage and the following variables: age, gender, right or left eye, axial length plus 237 different OCT measurements.Main outcome measuresThe area under the receiver operating characteristic curve (AROC) was then derived using the probability of glaucoma as suggested by the proportion of votes in the Random Forest classifier. For comparison, five AROCs were derived based on: (1) macular retinal nerve fibre layer (m-RNFL) alone; (2) circumpapillary (cp-RNFL) alone; (3) ganglion cell layer and inner plexiform layer (GCL+IPL) alone; (4) rim area alone and (5) a decision tree method using the same variables as the Random Forest algorithm.ResultsThe AROC from the combined Random Forest classifier (0.90) was significantly larger than the AROCs based on individual measurements of m-RNFL (0.86), cp-RNFL (0.77), GCL+IPL (0.80), rim area (0.78) and the decision tree method (0.75; p<0.05).ConclusionsEvaluating OCT measurements using the Random Forest method provides an accurate prediction of the presence of perimetric deterioration in glaucoma suspects.

show abstract

Correlation between nerve fibre layer thickness measured with spectral domain OCT and visual field in patients with different stages of glaucoma

Abstract: SD-OCT showed significantly decreased mean RNFL thickness of the inner ring surrounding the optic disc border of the ETDRS grid by 35% in PPG eyes and by 46% in eyes with early glaucoma compared to the control group. These results support the usefulness of this technology.

Cited by 42 publications

References 23 publications

iStent inject in phakic open angle glaucoma

iStent inject in phakic open angle glaucoma

Diagnostic Consistency and Relation Between Optical Coherence Tomography and Standard Automated Perimetry in Primary Open-Angle Glaucoma

Cross-sectional study: Does combining optical coherence tomography measurements using the ‘Random Forest’ decision tree classifier improve the prediction of the presence of perimetric deterioration in glaucoma suspects?

Contact Info

Product

Resources

About