Diagnostic Classification of Bruch's Membrane Opening-Minimum Rim Width and Retinal Nerve Fiber Layer Thickness in Myopic Eyes by Optical Coherence Tomography

Purpose: This study was conducted in order to compare the diagnostic classification of Bruch's membrane opening-minimum rim width (BMO-MRW) and RNFL thickness in normal myopic subjects by using optical coherence tomography (OCT).Methods: This cross-sectional study involved 75 healthy myopic subjects [spherical equivalent (SE) ≤ −0.5D] from April 2019 to January 2020. One eye of each subject was randomly selected for examination. BMO-MRW and peripapillary RNFL thickness were measured by spectral-domain OCT (Spectralis, Heidelberg Engineering GmbH, Heidelberg, Germany). All the subjects were divided into three groups: low myopic group (SE > −3D), moderate myopic group (−6D < SE ≤ −3D), and high myopic group (SE ≤ −6D). A nonparametric test was used to analyze the difference among groups. Linear regression was used to analyze the relationship between BMO-MRW/RNFL thickness and axial length/spherical equivalent. McNemar test was used to compare the diagnostic classification between BMO-MRW and RNFL thickness.Results: The RNFL thickness classified a significantly higher percentage of eyes as outside normal limits/borderline in at least 1 quadrant (BMO-MRW, 4%; RNFL thickness, 34.67%; p < 0.01). There was no significant correlation between BMO-MRW/RNFL thickness and AL/SE. The low myopia (SE > −3D) had a significantly lower percentage of eyes classified as outside normal limits/borderline in at least 1 quadrant than the moderate myopia (−6D < SE ≤ −3D) and high myopia (SE ≤ −6D) (low myopia, 12.5%; moderate/high myopia, 42.42%/50%; p < 0.05).Conclusion: BMO-MRW had a lower percentage of eyes classified as outside normal limits/borderline in at least 1 quadrant than RNFL thickness in normal myopic subjects. When referring to the diagnostic classification of RNFL thickness in myopic subjects, caution should be exercised in interpreting positive results. Further studies are needed to compare the diagnostic accuracy of these two measurements in myopic glaucoma patients.

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Prevalence of Glaucoma among High Myopia

QJM ◽

10.1093/qjmed/hcaa058.033 ◽

2020 ◽

Vol 113 (Supplement_1) ◽

Author(s):

O M Abdelfatah ◽

O A Salem ◽

A I Elawamry ◽

Y A Elzanklony

Keyword(s):

Optical Coherence Tomography ◽

Visual Field ◽

High Myopia ◽

Cross Sectional Study ◽

Automated Perimetry ◽

Optical Coherence ◽

Spherical Equivalent ◽

Sectional Study ◽

Cross Sectional ◽

Rnfl Thickness

Abstract Background Glaucoma is an optic neuropathy that is characterized by the selective loss of retinal ganglion cells and their axons, which manifests as the loss of the retinal nerve fiber layer (RNFL). Numerous studies have shown that the extent of RNFL damage correlates with the severity of functional deficit in the visual field (VF), and that RNFL measurement by optical coherence tomography (OCT) has good sensitivity for the detection of glaucoma. Purpose To assess the prevalence of glaucoma among high myopic patients and the association between them using standard automated perimetry (SAP) and optical coherence tomography (OCT). Patients and Methods A prospective observational randomized cross sectional study included a total of 80 eyes with high myopia, in the period from November 2017 to April 2018. This cross sectional study included 44 subjects with 80 eyes regarding high myopia using the outpatient services of the Qlawoon Hospital, Cairo, who satisfied the inclusion and exclusion criteria between November 2017 and April 2018 aiming to determine the prevalence of glaucoma in high myopic patients. Results In our study, we depended on the following highly significant parameters in detection of prevalence of glaucoma among high myopic patients: Spherical equivalent median is -12, Vertical cup/disc ratio mean is 0.55, MD median of visual field is – 5.38, PSD mean of visual field is 3.53, GHT is 64.7% outside normal limits, 17.6% border line and 17.6% general reduction of sensitivity and RNFL thickness mean is; for average thickness is 86.37, for superior thickness is 90.06 and for inferior thickness is 82.68 a highly significant P-value. Conclusion Prevalence of glaucoma among our study group is 42.5% depending on Spherical equivalent median, Vertical cup/disc ratio mean, MD median of visual field, PSD mean of visual field, GHT and RNFL thickness.

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Accuracy of a deep convolutional neural network in the detection of myopic macular diseases using swept-source optical coherence tomography

10.1101/2019.12.17.879650 ◽

2019 ◽

Author(s):

Takahiro Sogawa ◽

Hitoshi Tabuchi ◽

Daisuke Nagasato ◽

Hiroki Masumoto ◽

Yasushi Ikuno ◽

...

Keyword(s):

Optical Coherence Tomography ◽

Correct Answer ◽

High Myopia ◽

Binary Classification ◽

Area Under The Curve ◽

Optical Coherence ◽

Swept Source ◽

Study Results ◽

Sensitivity Specificity

AbstractThis study examined and compared outcomes of deep learning (DL) in identifying swept-source optical coherence tomography (OCT) images without myopic macular lesions [i.e., no high myopia (nHM) vs. high myopia (HM)], and OCT images with myopic macular lesions [e.g., myopic choroidal neovascularization (mCNV) and retinoschisis (RS)]. A total of 796 SS-OCT images were included in the study as follows and analyzed by k-fold cross-validation (k = 5) using DL’s renowned model, Visual Geometry Group-16: nHM, 107 images; HM, 456 images; mCNV, 122 images; and RS, 111 images (n = 796). The binary classification of OCT images with or without myopic macular lesions; the binary classification of HM images and images with myopic macular lesions (i.e., mCNV and RS images); and the ternary classification of HM, mCNV, and RS images were examined. Additionally, sensitivity, specificity, and the area under the curve (AUC) for the binary classifications as well as the correct answer rate for ternary classification were examined.The classification results of OCT images with or without myopic macular lesions were as follows: AUC, 0.983; sensitivity, 0.953; specificity, 0.940. The classification results of HM images and images with myopic macular lesions were as follows: AUC, 0.976; sensitivity, 0.940; specificity, 0.941. The correct answer rate in the ternary classification of HM images, mCNV images, and RS images were as follows: HM images, 93.7%; mCNV images, 82.4%; and RS, 92.3% with mean, 91.4%. Using noninvasive, easy-to-obtain swept-source OCT images, the DL model was able to classify OCT images without myopic macular lesions and OCT images with myopic macular lesions such as mCNV and RS with high accuracy. The study results suggest the possibility of conducting highly accurate screening of ocular diseases using artificial intelligence, which may improve the prevention of blindness and reduce workloads for ophthalmologists.

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Diagnostic Classification of Retinal Nerve Fiber Layer Measurement in Myopic Eyes: A Comparison Between Time-Domain and Spectral-Domain Optical Coherence Tomography

American Journal of Ophthalmology ◽

10.1016/j.ajo.2011.04.002 ◽

2011 ◽

Vol 152 (4) ◽

pp. 646-653.e2 ◽

Cited By ~ 20

Author(s):

Kun Liang Qiu ◽

Ming Zhi Zhang ◽

Christopher Kai-Shun Leung ◽

Ri Ping Zhang ◽

Xue Hui Lu ◽

...

Keyword(s):

Optical Coherence Tomography ◽

Nerve Fiber ◽

Retinal Nerve Fiber Layer ◽

Time Domain ◽

Spectral Domain ◽

Diagnostic Classification ◽

Optical Coherence ◽

Fiber Layer ◽

Nerve Fiber Layer

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Retinal thickness among normal myopic Filipinos using spectral domain optical coherence tomography

Asian Journal of Ophthalmology ◽

10.35119/asjoo.v17i2.537 ◽

2020 ◽

Vol 17 (2) ◽

pp. 156-169

Author(s):

Camille Elaine Zabala ◽

Jubaida Mangondato-Aquino ◽

Jose Ma. Martinez ◽

John Mark De Leon

Keyword(s):

Optical Coherence Tomography ◽

High Myopia ◽

Retinal Thickness ◽

Spectral Domain ◽

Pathologic Myopia ◽

Optical Coherence ◽

Fiber Layer ◽

Rnfl Thickness ◽

Thickness Measurements ◽

Sd Oct

Purpose: To determine mean macular and retinal nerve fiber layer (RNFL) thickness of myopic Filipinos using spectral domain optical coherence tomography (SD-OCT) and to evaluate influence of age, gender, and degree of myopia. Design: Observational clinic-based cohort. Methods: Participants were divided into two groups: low-moderate myopia [spherical equivalent (SE) -0.50 D to -6.00 D] and high-pathologic myopia (SE < -6.00 D and AL > 26.5 mm). Subgroup analyses between low myopia (refraction < -3.00 D or less) and moderate myopia (> -3.00 D to -6.00 D), and high myopia (> -6.00 D to -8.00 D) and pathologic myopia (more than -8.00 D) were done. Macular and RNFL thickness were measured by a SD-OCT and axial length (AL) with non-contact biometry. Results: Of 156 eyes, 88/156 (56%) had low-moderate myopia, 68/156 (44%) had high-pathologic myopia. There were 67/156 (43%) male and 89/156 (57%) female subjects. Mean central foveal subfield thickness measurements were 264 ± 24 μm for low myopia, 258 ± 17 μm for moderate myopia, 253 ± 25 μm for high myopia, and 218 ± 48 μm for pathologic myopia. Mean RNFL thickness measurements were 105.62 ± 3.89 μmfor low myopia, 97.6 ± 2.45 μm for moderate myopia, 85.9 ± 3.87 μm for high myopia, and 75.14 ± 3.89 μm for pathologic myopia. Average SE (p < 0.0001) decreased while AL (p < 0.0001) increased with more myopia. Myopia and age significantly affected macular and RNFL thickness parameters except for the following where only the degree of myopia was a significant factor: central foveal, temporal parafoveal, nasal perifoveal, inferior and nasal RNFL thicknesses. Conclusion: Retinal SD-OCT thickness measurements decreased with increasing level of myopia and age. Central foveal, temporal parafoveal, nasal perifoveal, inferior and nasal RNFL thicknesses may be more appropriate SD-OCT parameters among myopic Filipino patients to monitor for glaucoma since they may be less influenced by age.

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Correlation of Optical Coherence Tomographic Findings with Clinical Features in High Myopia

International Journal of Recent Surgical and Medical Sciences ◽

10.1055/s-0039-1689063 ◽

2019 ◽

Vol 05 (01) ◽

pp. 019-025

Author(s):

Evangeline Rao ◽

Maithili Mishra ◽

Sheela Kerkar ◽

Arjun Ahuja

Keyword(s):

Clinical Features ◽

Axial Length ◽

High Myopia ◽

Macular Thickness ◽

Optical Coherence ◽

Spherical Equivalent ◽

Fiber Layer ◽

Cross Sectional ◽

Rnfl Thickness ◽

Better Than

AbstractThis is a cross-sectional observational study to correlate relationship between macular and retinal nerve fiber layer (RNFL) thickness in relation to clinical features in high myopia. A total of 100 eyes of 50 consecutive patients underwent optical coherence tomography (OCT) of the macula and RNFL. It was observed that correlation of RNFL thickness to the axial length is better than that of RNFL thickness to the spherical equivalent. The macular thickness in the parafoveal region was observed to be thicker than the perifoveal region in all quadrants. This study therefore emphasizes the need to have macular thickness nomogram for high myopes to avoid misinterpretation of OCT results due to axial length and refractive error and, also, the need for a routine baseline OCT scan for all high myopia patients.

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Evaluation of retinal vascular density and related factors in youth myopia without maculopathy using OCTA

Scientific Reports ◽

10.1038/s41598-021-94909-8 ◽

2021 ◽

Vol 11 (1) ◽

Author(s):

Tiantian Wang ◽

Hui Li ◽

Rongrong Zhang ◽

Yan Yu ◽

Xin Xiao ◽

...

Keyword(s):

Optical Coherence Tomography ◽

Refractive Error ◽

Axial Length ◽

High Myopia ◽

Optical Coherence Tomography Angiography ◽

Flow Density ◽

Vascular Density ◽

Multivariate Linear Regression Analysis ◽

Optical Coherence ◽

Spherical Equivalent

AbstractTo evaluate the retinal vascular flow density changes of myopic eyes of young adults using optical coherence tomography angiography and the factors affecting these changes. In this cross-sectional study, 90 eyes of 45 participants were analyzed and divided into three groups: mild, moderate, and high myopia (without pathological changes). Macular and radial peripapillary capillary flow densities were measured using optical coherence tomography angiography. Their relationships with the axial length, the spherical equivalent of the refractive error, and age were analyzed using analysis of variance, Pearson’s correlation coefficient, and multivariate linear regression analysis. Superficial and deep macular vascular densities were significantly decreased in the high myopia group compared to the other groups. In the high myopia group, the nasal peripapillary flow density decreased, whereas the flow density inside the disc increased. The axial length negatively correlated with the superficial and deep macular vascular density, but positively correlated with the vascular density inside the disc. The spherical equivalent of the refractive error negatively correlated with the macular vascular density. The retinal vascular density decreased in the high myopia group. Hence, the microvascular network inside the disc may have a compensatory action in the hypoxic setting of high myopia.

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Repeatability of Spectral Domain Optical Coherence Tomography Measurements in High Myopia

Journal of Glaucoma ◽

10.1097/ijg.0000000000000385 ◽

2016 ◽

Vol 25 (5) ◽

pp. e526-e530 ◽

Cited By ~ 3

Author(s):

Harsha L. Rao ◽

Addepalli U. Kumar ◽

Sampath R. Bonala ◽

Kadam Yogesh ◽

Bodduluri Lakshmi

Keyword(s):

Optical Coherence Tomography ◽

High Myopia ◽

Spectral Domain ◽

Optical Coherence

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Development and validation of a deep learning system to screen vision-threatening conditions in high myopia using optical coherence tomography images

British Journal of Ophthalmology ◽

10.1136/bjophthalmol-2020-317825 ◽

2020 ◽

pp. bjophthalmol-2020-317825

Author(s):

Yonghao Li ◽

Weibo Feng ◽

Xiujuan Zhao ◽

Bingqian Liu ◽

Yan Zhang ◽

...

Keyword(s):

Optical Coherence Tomography ◽

Deep Learning ◽

High Myopia ◽

Large Scale ◽

Learning System ◽

Youden Index ◽

Optical Coherence ◽

Test Dataset ◽

Independent Test ◽

Independent Test Dataset

Background/aimsTo apply deep learning technology to develop an artificial intelligence (AI) system that can identify vision-threatening conditions in high myopia patients based on optical coherence tomography (OCT) macular images.MethodsIn this cross-sectional, prospective study, a total of 5505 qualified OCT macular images obtained from 1048 high myopia patients admitted to Zhongshan Ophthalmic Centre (ZOC) from 2012 to 2017 were selected for the development of the AI system. The independent test dataset included 412 images obtained from 91 high myopia patients recruited at ZOC from January 2019 to May 2019. We adopted the InceptionResnetV2 architecture to train four independent convolutional neural network (CNN) models to identify the following four vision-threatening conditions in high myopia: retinoschisis, macular hole, retinal detachment and pathological myopic choroidal neovascularisation. Focal Loss was used to address class imbalance, and optimal operating thresholds were determined according to the Youden Index.ResultsIn the independent test dataset, the areas under the receiver operating characteristic curves were high for all conditions (0.961 to 0.999). Our AI system achieved sensitivities equal to or even better than those of retina specialists as well as high specificities (greater than 90%). Moreover, our AI system provided a transparent and interpretable diagnosis with heatmaps.ConclusionsWe used OCT macular images for the development of CNN models to identify vision-threatening conditions in high myopia patients. Our models achieved reliable sensitivities and high specificities, comparable to those of retina specialists and may be applied for large-scale high myopia screening and patient follow-up.

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