Standard automated perimetry and algorithms for monitoring glaucoma progression

2008 ◽  
pp. 77-99 ◽  
Author(s):  
G.L. Scuderi ◽  
M. Cesareo ◽  
A. Perdicchi ◽  
S.M. Recupero
Keyword(s):  
Automated Perimetry ◽  
Standard Automated Perimetry ◽  
Glaucoma Progression

scholarly journals Monitoring for glaucoma progression with SAP, electroretinography (PERG and PhNR) and OCT

2021 ◽  
Author(s):  
Barbara Cvenkel ◽  
Maja Sustar ◽  
Darko Perovšek
Keyword(s):  
Optic Disc ◽  
Repeated Measurements ◽  
Photopic Negative Response ◽  
Ganglion Cell Complex ◽  
Automated Perimetry ◽  
Stable Group ◽  
Standard Automated Perimetry ◽  
Glaucoma Progression ◽  
The Mean ◽  
Over Time

Abstract Purpose To investigate the value of pattern electroretinography (PERG) and photopic negative response (PhNR) in monitoring glaucoma compared to standard clinical tests (standard automated perimetry (SAP) and clinical optic disc assessment) and structural measurements using spectral-domain OCT. Methods A prospective study included 32 subjects (32 eyes) with ocular hypertension, suspect or early glaucoma monitored for progression with clinical examination, SAP, PERG, PhNR and OCT for at least 4 years. Progression was defined clinically by the documented change of the optic disc and/or significant visual field progression (EyeSuite™ trend analysis). One eye per patient was included in the analysis. Results During the follow-up, 13 eyes (40.6%) showed progression, whereas 19 remained stable. In the progressing group, all parameters showed significant worsening over time, except for the PhNR, whereas in the stable group only the OCT parameters showed a significant decrease at the last visit. The trend of change over time using linear regression was steepest for the OCT parameters. At baseline, only the ganglion cell complex (GCC) and peripapillary retinal nerve fibre (pRNFL) thicknesses significantly discriminated between the stable and progressing eyes with the area under the ROC curve of 0.72 and 0.71, respectively. The inter-session variability for the first two visits in the stable group was lower for OCT (% limits of agreement within ± 17.4% of the mean for pRNFL and ± 3.6% for the GCC thicknesses) than for ERG measures (within ± 35.9% of the mean for PERG N95 and ± 59.9% for PhNR). The coefficient of variation for repeated measurements in the stable group was 11.9% for PERG N95 and 23.6% for the PhNR, while it was considerably lower for all OCT measures (5.6% for pRNFL and 1.7% for GCC thicknesses). Conclusions Although PERG and PhNR are sensitive for early detection of glaucomatous damage, they have limited usefulness in monitoring glaucoma progression in clinical practice, mainly due to high inter-session variability. On the contrary, OCT measures show low inter-session variability and might have a predicting value for early discrimination of progressing cases.

Applications of deep learning in detection of glaucoma: A systematic review

2020 ◽  
pp. 112067212097734
Author(s):  
Delaram Mirzania ◽  
Atalie C Thompson ◽  
Kelly W Muir
Keyword(s):  
Systematic Review ◽  
Deep Learning ◽  
Clinical Settings ◽  
Automated Perimetry ◽  
Functional Tests ◽  
Standard Automated Perimetry ◽  
Glaucoma Progression ◽  
Novel Technique ◽  
Fundus Photographs ◽  
Level Performance

Glaucoma is the leading cause of irreversible blindness and disability worldwide. Nevertheless, the majority of patients do not know they have the disease and detection of glaucoma progression using standard technology remains a challenge in clinical practice. Artificial intelligence (AI) is an expanding field that offers the potential to improve diagnosis and screening for glaucoma with minimal reliance on human input. Deep learning (DL) algorithms have risen to the forefront of AI by providing nearly human-level performance, at times exceeding the performance of humans for detection of glaucoma on structural and functional tests. A succinct summary of present studies and challenges to be addressed in this field is needed. Following PRISMA guidelines, we conducted a systematic review of studies that applied DL methods for detection of glaucoma using color fundus photographs, optical coherence tomography (OCT), or standard automated perimetry (SAP). In this review article we describe recent advances in DL as applied to the diagnosis of glaucoma and glaucoma progression for application in screening and clinical settings, as well as the challenges that remain when applying this novel technique in glaucoma.

scholarly journals The Assessment of Morphological and Functional Changes in the Detection of the Initial Stage of Primary Glaucoma

2021 ◽  
Vol 18 (4) ◽  
pp. 857-865
Author(s):  
N. I. Kurysheva ◽  
L. V. Lepeshkina
Keyword(s):  
Open Angle Glaucoma ◽  
Angle Closure ◽  
Ganglion Cell Complex ◽  
Automated Perimetry ◽  
Standard Automated Perimetry ◽  
Fiber Layer ◽  
Glaucoma Progression ◽  
Functional Changes ◽  
Primary Glaucoma ◽  
Sd Oct

Purpose — to study morphological and functional changes in the detection of primary glaucoma progression.Patients and methods. 128 patients (128 eyes, among them — 64 eyes with primary open angle glaucoma (POAG) and 64 with primary angle closure glaucoma (PACG)) with the initial MD of –6.0 dB were examined at the Ophthalmology Center of the FMBA of Russia from May 2016 to November 2019. The values of corneal-compensated IOP were also considered: minimal (IOPmin), peak (IOPmax) and its fluctuations (IOPfluct). The progression was measured using standard automated perimetry (SAP) and spectral-domain OCT (SD-OCT). During the observation period, each patient received the average of 8.42 ± 2.08 SAP and SD-OCT. Progressive thinning of the retinal nerve fiber layer (RNFL) and its ganglion cell complex (GCC) were evaluated using SD-OCT. If RNFL and/or GCC had a trend of significant (p < 0.05) thinning, the eye was classified as having the SD-OCT progression. The correlation between the rate of progression detected by SAP (ROP1) using thinning of RNFL (ROP2) and GCC (ROP3) with other clinical parameters was analyzed.Results and discussion. Glaucoma progression was detected in 73 eyes. While the isolated use of SAP did not allow detecting progression, it was possible to detect it in 39 % cases by SD-OCT. The combination of both methods allowed detecting progression in 57 %. In both forms, ROP1 correlated with IOPmin: in PACG r = 0.41, p = 0.023 and in POAG r = 0.43, p = 0.016. In PACG, ROP2 and ROP3 correlated with the foveal choroid thickness: r = 0.46, p = 0.019 and r = 0.47, p = 0.009, respectively. At the same time, ROP3 was associated with peak IOP (r = –0.402, p = 0.025); the correlation of peak IOP with its fluctuations amounted to 0.7 (p < 0.001).Conclusion. SD-OCT is more informative than SAP in determining the progression of the initial primary glaucoma. The combination of these two methods 1.5 times increases the possibility of detecting progression in comparison with the isolated use of SD-OCT. The choroid thickness, associated with the IOP fluctuations, plays an important role in the progression of PACG.

Glaucoma diagnostic performance of humphrey matrix and standard automated perimetry

2009 ◽  
Vol 53 (5) ◽  
pp. 482-485
Author(s):  
Yoon Pyo Nam ◽  
Seong Bae Park ◽  
Sung Yong Kang ◽  
Kyung Rim Sung ◽  
Michael S. Kook
Keyword(s):  
Diagnostic Performance ◽  
Automated Perimetry ◽  
Standard Automated Perimetry

scholarly journals Association between visual field damage and corneal structural parameters

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Alexandru Lavric ◽  
Valentin Popa ◽  
Hidenori Takahashi ◽  
Rossen M. Hazarbassanov ◽  
Siamak Yousefi
Keyword(s):  
Machine Learning ◽  
Visual Field ◽  
Structural Parameters ◽  
Corneal Thickness ◽  
Anterior Segment ◽  
Mean Deviation ◽  
Automated Perimetry ◽  
Standard Automated Perimetry ◽  
Visual Field Damage ◽  
Corneal Shape

AbstractThe main goal of this study is to identify the association between corneal shape, elevation, and thickness parameters and visual field damage using machine learning. A total of 676 eyes from 568 patients from the Jichi Medical University in Japan were included in this study. Corneal topography, pachymetry, and elevation images were obtained using anterior segment optical coherence tomography (OCT) and visual field tests were collected using standard automated perimetry with 24-2 Swedish Interactive Threshold Algorithm. The association between corneal structural parameters and visual field damage was investigated using machine learning and evaluated through tenfold cross-validation of the area under the receiver operating characteristic curves (AUC). The average mean deviation was − 8.0 dB and the average central corneal thickness (CCT) was 513.1 µm. Using ensemble machine learning bagged trees classifiers, we detected visual field abnormality from corneal parameters with an AUC of 0.83. Using a tree-based machine learning classifier, we detected four visual field severity levels from corneal parameters with an AUC of 0.74. Although CCT and corneal hysteresis have long been accepted as predictors of glaucoma development and future visual field loss, corneal shape and elevation parameters may also predict glaucoma-induced visual functional loss.

scholarly journals Ganglion cell complex scan in the early prediction of glaucoma

2012 ◽  
Vol 4 (2) ◽  
pp. 236-241
Author(s):  
S Ganekal
Keyword(s):  
Ganglion Cell ◽  
Cell Complex ◽  
P Value ◽  
Ganglion Cell Complex ◽  
Automated Perimetry ◽  
Standard Automated Perimetry ◽  
Fiber Layer ◽  
Color Code ◽  
Rnfl Thickness ◽  
Significant Difference

Objective: To compare the macular ganglion cell complex (GCC) with peripapillary retinal fiber layer (RNFL) thickness map in glaucoma suspects and patients. Subjects and methods: Forty participants (20 glaucoma suspects and 20 glaucoma patients) were enrolled. Macular GCC and RNFL thickness maps were performed in both eyes of each participant in the same visit. The sensitivity and specificity of a color code less than 5% (red or yellow) for glaucoma diagnosis were calculated. Standard Automated Perimetry was performed with the Octopus 3.1.1 Dynamic 24-2 program. Statistics: The statistical analysis was performed with the SPSS 10.1 (SPSS Inc. Chicago, IL, EUA). Results were expressed as mean ± standard deviation and a p value of 0.05 or less was considered significant. Results: Provide absolute numbers of these findings with their units of measurement. There was a statistically significant difference in average RNFL thickness (p=0.004), superior RNFL thickness (p=0.006), inferior RNFL thickness (p=0.0005) and average GCC (p=0.03) between the suspects and glaucoma patients. There was no difference in optic disc area (p=0.35) and vertical cup/disc ratio (p=0.234) in both groups. While 38% eyes had an abnormal GCC and 13% had an abnormal RNFL thickness in the glaucoma suspect group, 98% had an abnormal GCC and 90% had an abnormal RNFL thickness in the glaucoma group.Conclusion: The ability to diagnose glaucoma with macular GCC thickness is comparable to that with peripapillary RNFL thickness. Macular GCC thickness measurements may be a good alternative or a complementary measurement to RNFL thickness assessment in the clinical evaluation of glaucoma.DOI: http://dx.doi.org/10.3126/nepjoph.v4i2.6538 Nepal J Ophthalmol 2012; 4 (2): 236-241 

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